S. Oreshkin, A. Spesivtsev, I. Daimand, V. Kozlovsky, V. Lazarev, Automation in industry. 2013. No. 7

A new solution to the problem of building an intelligent process control system (IASUTP) is considered, combining the use of unique methodologies: building a semantic network based on a basic ontology and polynomial transformation of non-factors, the essence of which is to transform the qualitative knowledge of an expert into a mathematical model in the form of a nonlinear polynomial function.

The Summa Technologies company proposes a new solution to the problem of building an intelligent process control system (IASUTP), combining the use of unique methodologies: building a semantic network based on a basic ontology that allows you to describe a complex multi-factor model in the form of a semantic network on a specific limited dictionary, and polynomial transformation of non-factors, the essence of which is to transform the qualitative knowledge of an expert into a mathematical model in the form of a non-linear polynomial function. The first of the methodologies has the property of universality regardless of the subject area, and the second one conveys the specifics of this area through the experience and knowledge of experts. The results of industrial testing of the developed IACS in relation to the process of smelting copper-nickel sulfide raw materials at the Copper Plant of the Polar Branch of OJSC MMC Norilsk Nickel (Norilsk), which has the properties of a "complex system" and operates under conditions of "significant uncertainty", are presented.

Introduction

Analyzing tasks automated control most technological processes in various industries (chemical, ferrous and non-ferrous metallurgy, mining and oil and gas production, thermal power, Agriculture etc.), it is possible to single out the problem that unites them, which consists in the need to build such a mathematical model of technological processes that will allow taking into account all the required input information, taking into account its possible inaccuracy, uncertainty, incompleteness, and at the same time obtaining data at the output (control action , forecast), adequate to the current situation in the technological process .

It is known that the traditional approach to modeling (that is, modeling based on traditional methods assuming the completeness and accuracy of knowledge about the process) is practically inapplicable when considering complex multifactorial processes that are generally difficult to formalize. The complexity of real processes determines the search non-traditional methods building their mathematical models and optimizing their control. At the same time, not only the aspect of optimal control is very important, but also the aspect of analyzing the current state of the process, since it is precisely the conclusion about current state process allows you to choose the optimal control in this situation. Such an analysis can be performed on the basis of a system of structural-flow-multilevel recognition of the technical state of the process in real time.

The main factor that devalues ​​attempts to build formal models and describe the technical state of such complex processes using traditional methods is the "significant uncertainty" of the input information. This is manifested in the objective impossibility of stabilizing and/or measuring the values ​​of a number of key parameters of the technical state of such processes. The consequence of this is a violation of the main criteria for the technological consistency of the process, which affects both the quality of the final products and the stability of the process as a whole. In the language of mathematics, such processes are referred to as "complex technical systems" or "weakly structured systems", for which there is currently no general theory of modeling.

The traditional process control system aims to automate the maintenance of a unit or a processing unit, and its functions, by definition, do not include issues of optimal process control and analysis of its state. For example, the process control system allows you to change the position of the control mechanisms serving the unit, monitors the connected operation of the units of the unit, allows you to change the performance of the unit and its mode of operation. But the state of the process, the quality of the final products, the ratio of incoming products by elemental composition - these issues are often outside the basic automation of the unit. Thus, in the presence of only a basic process control system, the operator is forced to perform the functions of servicing not only the unit, but also the process taking place in it. This is what leads to the “human factor” problem, since the operator does not always manage to fully achieve all, most often multidirectional, management goals. In addition, the design features of the unit do not always allow to fully resolve all issues at the level of process control systems. An example of this is the problem of ensuring the necessary reliability of input information in the current version of the process control system when assessing the quality and quantity of materials supplied to the reaction zone in real time.

Intelligent ACS (IACS) is a system that uses the basic automation of the unit as a source of input information and allows, based on artificial intelligence technologies, to build a model of the process occurring in the unit, analyze the current state of the process according to the model and, based on the analysis, solve the problem of optimal control of a given unit.

The existing so-called "turnkey" "boxed solutions" imply the need for complete automation of the unit or redistribution "from scratch". At the same time, both the hardware component of automation and software are supplied to the customer. The functionality of such a solution can be quite wide, including containing an intellectual component, but at the same time completely incompatible with existing ones. currently Process control system of the customer. This often leads to a sharp complication and an increase in the cost of the technical solution. The proposed option for building an intelligent automated control system based on expert knowledge, using basic automation, aims to monitor and control the process occurring in the unit. Such a system under conditions of “significant uncertainty” is able to evaluate unmeasurable or poorly measured parameters, interpret them quantitatively accurately enough, identify the current technical state of the process and recommend the optimal control action to eliminate the conflict that has arisen (in case of conflicts in the technological consistency of the process).

IACS in this version, using intelligent technologies, allows you to:

• to carry out integration with any basic automated control system that already exists on the unit or the customer's redistribution;
• implement the creation of a common information space for all redistribution units in order to implement common management and monitoring;
• perform a quantitative assessment of non-measurable and / or qualitative parameters on each unit within the framework of the basic ACS of the unit;
• track the criteria for technological consistency of the process both for each individual unit and (if necessary) for the processing unit as a whole;
• assess the current state of technological processes both for each individual unit and for the processing unit as a whole in real time;
• to develop control decisions - advice to the operator regarding the restoration of the technological balance both for the unit and for the redistribution as a whole.

The basis of the intellectual core of IACS is the method of knowledge representation "Semantic network on the base ontology", which allows describing a complex multifactorial model in the form of a semantic network on a specific limited dictionary, and the method "Polynomial transformation of non-factors", the essence of which is to transform the expert's qualitative knowledge into mathematical model as a non-linear polynomial function .

The purpose of this article is to familiarize readers with a new approach to solving the problem of building an IACS based on the use of unique methodologies and the results of commercial operation of the IACS PV-3 of the Copper Plant of the Polar Branch of OJSC MMC Norilsk Nickel. IASUTP was developed by Summa Technologies in 2011–2012. based on the G2 platform from Gensym (USA) to control the Vanyukov process for the processing of sulfide copper-nickel raw materials.

Technological process as an object of modeling

Most technological processes, including the Vanyukov process, have all the features of "complex technical systems» - multiparametric and «significant uncertainty» of the input information. Under such conditions, to solve the problem of maintaining the technological consistency of TP, it is advisable to use the methods of expert assessment of the situation and the formation of a conclusion based on the knowledge and experience of an expert.

Summa Tekhnologii developed the IACS of the Vanyukov Furnace (IACS PV-3) of the Copper Plant of the Polar Branch of OJSC MMC Norilsk Nickel based on the G2 platform from Gensym (USA) to solve the following tasks for controlling the Vanyukov process:

• stabilization of the quality of smelting products;
• quantitative assessment of non-measurable or poorly measured (due to a number of both objective and subjective reasons) parameters technological process and states of aggregates by indirect methods;
• reducing the energy intensity of the processing of various charge materials;
• stabilization temperature regime process while saving planned assignments and goals.

On fig. 1 shows the layout of the main structural elements of the PV. The unit is a rectangular coffered water-cooled shaft 2 located on the bottom 1, in the roof of which there are two chutes 3 for supplying charge materials into the melt, and to which matte 4 and slag 5 siphons with drain holes 9 and 10, respectively, adjoin from the side of the end walls. An uptake 6 is provided for the evacuation of gases. Charge materials through chutes 3 enter the melt, which is blown with an oxygen-air mixture (OAC) through tuyeres 7, intensively bubbling the matte-slag emulsion in the above-tuyere zone. Oxygen KVS oxidizes iron sulfide, thereby enriching the matte "beads" (drops), segregating into the bottom part due to the difference in densities of immiscible liquids of matte and slag. At the same time, the movement of mass flows of the melt is directed downward due to the continuous release of matte 4 and slag 5 from the siphons through the outlet holes 9 and 10, respectively. Due to the design features shown in Fig. 1, the Vanyukov process itself is also implemented, the main idea of ​​which is clear from the above description.

It should be noted the features of the Vanyukov process, which distinguish it from other, including foreign, pyrometallurgy technologies: high specific productivity - up to 120 tons per 1 m2 of the bath mirror area per day (melting up to 160 t/h); small dust removal -< 1%; переработку шихты крупностью до 100 мм и влажностью > 16%.

The software and hardware complex, on the basis of which the APCS PV-3 is implemented, has a three-level architecture. The lower level includes sensors, electric actuators, control valves, actuators, average level- PLC, upper - personal electronic computers (PC). Based on the workstation, a graphical interface was implemented for the interaction of the operator with the control system, an audible alarm system, and storage of the process history (Fig. 2).

The melting process is controlled from the operator's workstation ("console"). In this case, not only information from sensors and actuators is used, but also organoleptic information, when the melter, observing the characteristic features of the behavior of the melt pool (the magnitude and "severity" of splashes, the general condition of the bath, etc.), transfers the obtained estimates to the operator's console. All these sources of information, heterogeneous in their physical essence, together allow the operator to evaluate the current situation by many variables, for example, "Loading", "Height of the pool", "Melt temperature", etc., which determine more generalized concepts: "The state of the melt pool", "The state of the process as a whole".

Objectively arising working conditions often lead to stricter requirements for the Vanyukov process; for example, to the need to melt a large amount of technogenic raw materials, which greatly complicates the task of maintaining the technological consistency of the process, since technogenic components are poorly predictable in composition and moisture content. As a result, the operator, not having sufficient information about the properties of such raw materials, is not always able to make the right decisions and "loses" either the temperature or the quality of the final products.

The basis of the developed IACS PV-3 is the principle of conducting the process in a rather narrow “corridor” according to the main criteria of technological consistency of the process in order to improve the quality of the final product and maintain the operational properties of the unit. IACS PV-3 is designed for early forecasting and informing the operator about violations of technological consistency at the initial stages of their inception by analyzing special criteria developed on the basis of expert knowledge. Criteria set the goals of process control and inform the operator about the current state of the process. At the same time, if the values ​​of the criteria go beyond the permissible limits, they are interpreted by the system as the beginning of a “conflict”, and for the operator they are a signal of the need to take the recommended control actions to return the process to a state of technological consistency.

Brief description of system features

IACS PV-3, based on the initial information received from the APCS PV-3 and other information systems, implements the Vanyukov process model in real time, analyzes the current state of the process for the presence of technological imbalances and, in case of conflicts, identifies them, offering conflict resolution scenarios to the operator. The system thus acts as an “advisor to the operator”. IAMS visualizes information channels that display to the user the current state of control criteria and forecasts for the quality of final products.

IASU PV-3 has the following consumer characteristics:

• intuitive user interface for technological personnel;
• software and information compatibility with ACS PV-3 and others information systems;
• the ability to adapt the system to other units at the level of filling the knowledge base without changing the software core of the system;
• localization of all user interface elements in Russian;
• reliability, openness, scalability, that is, the possibility of further expansion and modernization.

Control and management of all units and actuators is carried out from the stations of the operator of the automatic control system PV-3, located in the operator's room PV-3.

In addition to the existing operator stations, a specialized workstation is used, designed to provide the operator with the user interface of the IACS PV-3 system. Architecturally and functionally, IACS PV-3 looks like an addition to the existing APCS PV-3, that is, as an extension of the functional and information functions of the existing control system.

IACS PV-3 provides real-time execution of the following application functions:

• assessment of the quantity and quality of the charge supplied to the furnace charge;
• forecast of the quality of final products;
• displaying the results of the decisions made by the operator according to the criteria of technological balance of the process;
• automatic analysis quality of process control;
• accumulation of a knowledge base on management for the entire period of operation of the system;
• simulation of the PV-3 unit for use in the "Simulator" mode for the purpose of personnel training.

Architecture IASU PV-3

IASU PV-3 is an expert system that implements intelligent monitoring and control of the melting process in the mode of advice to the operator. The control is implemented as a set of recommendations to the operator and the senior smelter to maintain the technological balance of the process while meeting the goals set for the quality of the final melting products, obtaining a given amount finished products(ladles of matte) and melting of technogenic materials.

The main elements of IACS PV-3, as well as any expert system, are: knowledge base; block decision making; block of recognition of the input information flow (obtaining an output on knowledge). On fig. 3 shows a generalized architecture of the system.

The uniqueness of the methodology for extracting and presenting expert knowledge in the form of a nonlinear polynomial makes it possible to synthesize in the shortest possible time a sufficient system of logical and linguistic models that systematically represents the features of the flow of technological processes. At the same time, the use of highly qualified specialists as experts, who operate this particular unit with its characteristic features, guarantees the conduct of the process taking place in it in accordance with the technological instructions of the enterprise.

The knowledge representation for the description of the Vanyukov process model is based on the representation “Semantic network on the base ontology”. This representation involves the selection of a dictionary - the basic ontology based on the analysis of the subject area. Using the basic ontology and a set of features corresponding to the elements of the basic ontology, it is possible to build a semantic network that allows you to structure a complex multi-factor model. Thanks to such a description, on the one hand, a significant reduction in the dimension in terms of the number of factors is achieved, and, on the other hand, the links by which these factors are interconnected are unified. At the same time, the semantics and functionality of each of the considered factors is completely preserved.

All knowledge about the Vanyukov process and about the PV-3 unit, in which this process is implemented, is stored in the knowledge base (KB). The latter is designed as a relational data warehouse and contains a formal record of knowledge in the form of records in tables.

The knowledge processor or decision block as part of the expert system is implemented on the basis of the platform for the development of industrial expert systems G2 (Gensym, USA). The main elements of the knowledge processor (Fig. 3) are the blocks: recognition of the input information flow; calculation of the model according to the current situation; situational analysis; decision making.

Let's take a closer look at these elements. At the moment of launching the expert system, the knowledge processor reads all the information from the knowledge base that is stored in the repository and builds a model of the PV-3 aggregate and the Vanyukov process. Further, as the process and the PV-3 unit work, data from the ACS of the unit enters the IACS system. These data characterize both the state of the process (specific oxygen consumption per ton of metal-containing, etc.) and the state of the PV-3 unit (the temperature of the outgoing water from the caissons of each row, the state of the lances for supplying blast to the melt, etc.). The data enters the recognition block, is identified in terms of technological consistency criteria, and then, based on these data, a calculation is performed according to the Vanyukov process model. The results of this calculation are analyzed in the situational analysis block, and in the event of a violation of the technological balance, the situation is identified by the system as “conflict”. Further, a decision is made regarding the restoration of the technological balance. The solutions obtained, as well as information about the current state of the process, along with information about conflicts, are displayed in the client module of the IACS PV-3 (Fig. 4). The model is updated every minute.

Practical implementation

We will demonstrate the predictive capabilities of the IACS PV-3 during its operation at the Copper Plant of the Polar Branch of OJSC MMC Norilsk Nickel.

On fig. Figure 4 shows the IACS PV-3 interface, the information of which serves as an addition to the main ACS for the operator (Fig. 2) when making a control decision. Field 1 (Fig. 4) visualizes the values ​​of calculations according to the model "Specific oxygen consumption per ton of metal-bearing". The reflection of the predictive ability of IACS PV-3 on the quality of the final product - the copper content in the matte - shows the graph of field 2, and on silicon dioxide - fields 3. As indicators, the panel contains: 4 - copper content in slag (%); 5 - percentage of fluxes in the load from metal-containing; 6 - download quality (w/r); 7 - melt temperature (°C). Field 8 contains the hourly calculated values ​​of charge materials consumption by bunkers, and field 9 reflects the names of conflicts that take place in this moment time. An increase in the accuracy of calculations for models is facilitated by switching to the appropriate control mode with the radio buttons of field 10. The fact of pouring converter slag is taken into account by the button of field 11.

Analysis of the minute-by-minute values ​​of the graph in field 1 shows the stable conduct of the process within acceptable limits according to the criterion of specific oxygen consumption per ton of metal-containing, beyond which the loss of quality of the end products is guaranteed. Thus, being outside the designated boundaries for more than 10 minutes can lead to critical states of the process: below 150 m3/t - under-oxidation of the melt and, as a result, cold running of the furnace; above 250 m3/t - overoxidation of the melt, and as a result, the hot running of the furnace.

The calculated copper content in the matte according to the actual data (field 2) clearly correlates with the behavior of the values ​​of the previous criterion (field 1).

So, in the time interval 17:49–18:03, the peaks on both graphs coincide, which reflects the fact that the system responds to a change in the physical and chemical state of the HP: the regular operation of furmation (cleaning) of the blast supply devices into the melt led to an increase in the specific oxygen consumption > 240 m3/t, caused a natural increase in the temperature of the melt and, thus, caused a natural increase in the copper content in the matte.

In addition, the conduct of the process at a specific oxygen consumption in the region of 200 m3/t naturally determines the copper content in the matte 57...59% during the observed 2 hour interval.

Comparison of the behavior of the blue and green graphs (field 1) indicates that the operator follows the recommendations of the system almost all the time. At the same time, the actual values ​​of the “Specific consumption” criterion differ from those recommended due to a) natural fluctuations in the readings of the sensors of the PV-3 unit in terms of blast consumption; b) the technological operation of the furmation of the furnace (peak on the graph); c) chemical changes in the state of the melt bath due to fluctuations in the composition of the raw material. Let's pay attention to the fact that according to the criterion "% of fluxes from metal-containing", the operator works with an overrun (yellow zone of the indicator 5) relative to the recommendations of the system. A similar situation is associated with the presence of technogenic raw materials in the feed. As a result, fluctuations in the content of silicon dioxide in the melt become difficult to predict, and the system warns the operator that prolonged operation in this mode of flux loading can lead to process imbalance. The fact of the presence of technogenic raw materials in the composition of the load is also confirmed by the calculated parameter "Load quality" (indicator 6), which displays the value in the red zone - "Not high-quality raw materials".

Thus, the system guides the operator in terms of conducting the process in a “narrow” corridor of values ​​of the main technological parameters of consistency, while indicating what quality the product will be obtained as a result of melting.

Keeping the process within the given limits of the main technological criteria also makes it possible to optimize the blast operation of the furnace, in particular, to reduce the consumption of natural gas in the blast.

The visualization of trends according to the main criteria has, in addition, a positive psychological impact on the operator-technologist, since it “justifies” in a quantitative form the implementation of the decision made in process control.8 9

Conclusion

Developed by Summa Tekhnologii and tested at the Copper Plant of Polar Division MMC Norilsk Nickel automated system monitoring and control of the Vanyukov process IACS PV-3 as a "complex technical system" allows us to make some generalizations in relation to the use of the results obtained in other branches of knowledge and industry.

The synthesis of the above independent technologies makes it possible to create an IACS of almost any "complex technical system" in the presence of the customer's existing basic automation and highly qualified specialists who effectively operate such systems under conditions of "substantial uncertainty".

The proposed approach to the construction of IACS has several more advantages. Firstly, it provides significant time savings due to the fact that the first technology (using the ontological approach) is already implemented in the software product and allows you to process knowledge about any models in the knowledge base, and the second (building a system of mathematical equations of a complex technological process) in due to the formula development of the method of application, it requires a minimum of calls to an expert. Secondly, the use of expert knowledge in relation to the assessment of the technical condition of a particular object is carried out under conditions technological regulations its functioning, which minimizes the degree of risk of the system developing the wrong decision, and real-time monitoring contributes to the early detection of approaching the outrageous (pre-emergency) states of the process. Thirdly, the most general approach to solving the multilevel recognition of the technical state of complex technological processes, objects or phenomena in any industry is actually implemented - non-ferrous and ferrous metallurgy, mining and oil and gas production, chemical industry, thermal power engineering, agriculture, etc.

Bibliography

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2. Spesivtsev A.V. Metallurgical process as an object of study: new concepts, consistency, practice. - St. Petersburg: Publishing House of the Polytechnic. un-ta, 2004. - 306 p.

3. Spesivtsev A. V., Lazarev V. I., Daimand I. N., Negrey D. S. Estimation of the degree of consistency of the functioning of the technological process based on expert knowledge.//Sb. reports. XV International Conference on Soft Computing and SCM Measurements. St. Petersburg, 2012, T. 1. - S. 81–86.

4. Okhtilev M.Yu., Sokolov B.V., Yusupov R.M. Intelligent technologies for monitoring and controlling the structural dynamics of complex technical objects. - M.: Nauka, 2006. - 410 p.

5. Narinyani A.S. Non-factors and knowledge engineering: from naive formalization to natural pragmatics//KII 94. Sat. works. Rybinsk, 1994. - S. 9–18.

6. Spesivtsev A.V., Domshenko N.G. Expert as an "intelligent measuring and diagnostic system".//Sat. reports. XIII International Conference on Soft Computing and SCM Measurements. St. Petersburg, 2010, T. 2. - S. 28–34.

7. Vanyukov A.V., Bystrov V.P., Vaskevich A.D. and other Melting in a liquid bath / Ed. Vanyukova A. V. M.: Metallurgy, 1988. - 208 p.

Sources of financing of investment activity. Analysis of the structure and dynamics of property and sources of its formation. The main directions of increasing investment attractiveness: increasing the profit of the organization by expanding the sales market.

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Ministry of Education and Science Russian Federation

Federal State Budgetary Educational Institution

higher education

TOMSK STATE UNIVERSITY OF CONTROL SYSTEMS AND RADIO ELECTRONICS (TUSUR)

Department of Economics

Evaluation of the investment attractiveness of an organization (on the example of Synthesis of Intelligent Systems LLC)

Undergraduate work

in the direction 38.03.01 - Economics profile "Finance and Credit"

Final qualifying work 73 pages, 5 figures, 16 tables, 23 sources.

Object of study - Society with limited liability"Synthesis of intelligent systems".

The purpose of the work is to evaluate the investment attractiveness of the SIS LLC organization and offer recommendations for its improvement.

To achieve this goal, the following tasks were solved:

The theory of investment attractiveness is analyzed, the essence of the concept of investments and their classification, the concept of investment attractiveness are determined;

Analyzed methods for assessing the investment attractiveness of the organization;

An assessment of the investment attractiveness of the organization SIS LLC was carried out on the basis of financial and economic indicators;

The main directions for increasing investment attractiveness are proposed, namely: increasing the profit of the organization by expanding the sales market.

The information base of the study, within the framework of this final qualification work, was made up of: data from the enterprise's financial statements, information posted on the official website of the organization, research materials of scientists published in scientific journals, scientific articles V periodicals, study guides, as well as information resources of the Internet.

Final qualification work 73 pages, 5 drawings, 16 tables, 23 sources.

The object of the research is the company Limited Liability Company "Synthesis of intelligent systems"

The purpose of the work is to assess the investment attractiveness of the organization SIS LLC and propose recommendations for improving it.

To achieve this goal, the following tasks were accomplished:

The theory of investment attractiveness is analyzed, the essence of the concept of investments and their classification, the concept of investment attractiveness are defined;

Methods for evaluating the investment attractiveness of the organization are analyzed;

An assessment of the investment attractiveness of the organization "SIS" on the basis of financial and economic indicators;

The main directions of increasing the investment attractiveness are proposed, namely: increase of the profit of the organization due to the expansion of the sales market.

Information base of the research, within the framework of this final qualifying work, was: data of the enterprise's accounting reports, information posted on the official website of the organization, research materials of scientists published in scientific journals, scientific articles in periodicals, teaching aids, and information resources of the network The Internet.

INTRODUCTION

In modern conditions, organizations of various forms of ownership are puzzled by increasing their productivity, competitiveness, profitability and financial independence in the long term, which directly depends on the current level of investment activity of the organization, its coverage investment activity and investment attractiveness.

Investment attractiveness is an indicator by which investors make decisions about investing their funds in a particular organization.

The relevance of the chosen topic is due to the fact that potential investors, as well as managers, need to have a clear model for assessing the investment attractiveness of an organization for the most effective management or investment decision. Also, the level of investment attractiveness is important for creditors and customers, the former are interested in the creditworthiness of the organization, and the latter - in the reliability of business relations, the continuity and stability of the organization's activities, which depend on the liquidity and financial stability of the organization.

The set of indicators selected for evaluation

investment attractiveness depends on the specific goals of the investor.

The importance of determining the investment attractiveness of organizations is beyond doubt, since without this there will be no investment in economic entities and, as a result, economic growth and stabilization will not be possible. In some cases, investments are the lifeblood of the organization as a whole.

Financial analysis, as the main mechanism that ensures the financial stability of the organization and the assessment of its attractiveness for potential investors, is the central link in the methodology for determining investment attractiveness. Its main purpose is to study the problems that arise when assessing the financial attractiveness of an organization for an investor. In this regard, aspects of the analysis of the financial condition of the organization are considered, an assessment of the level of profitability, creditworthiness, efficiency and financial stability is carried out.

The result of the financial analysis is the determination of the main directions for increasing the investment attractiveness of the analyzed organization.

aim thesis is the study theoretical aspects concerning the concept of investment attractiveness and methods for its assessment, directly assessing investment attractiveness on the example of the organization Synthesis of Intelligent Systems LLC, as well as developing recommendations for improving the investment attractiveness of the organization.

To achieve this goal, it is necessary to solve the following tasks:

Determine the essence and give a classification of investments;

To study methods for assessing the investment attractiveness of an organization;

To assess the investment attractiveness of the organization based on the chosen methodology;

The object of the study is the organization LLC Synthesis of Intelligent Systems.

1. THEORETICAL FOUNDATIONS OF THE INVESTMENT ACTIVITY OF THE ORGANIZATION

1.1 Essence and classification of investments

A common understanding of the essence of investment as economic category among scientists and economists does not exist. There are different interpretations that differ in meaning, some of which do not convey the whole essence of this term.

According to federal law dated February 25, 1999 N 39-FZ "On investment activities in the Russian Federation, carried out in the form of capital investments" "... investments - cash, securities, other property, including property rights, other rights having a monetary value, invested in objects of entrepreneurial and (or) other activities in order to make a profit and (or) achieve another beneficial effect.

Based on the versatility of interpretations of the term, it is possible to single out the economic and financial definitions of investments. Economic definition characterize investments as a set of costs realized in the form of long-term capital investments in various sectors of the economy of the production and non-production spheres. From a financial point of view, investments are all types of resources invested in economic activity with the aim of generating income or benefits in the future.

In general, investments are understood as the investment of capital in all its forms with the aim of generating income in the future or solving certain problems.

The organization may or may not carry out investment activities, but the failure to carry out such activities leads to the loss of competitive positions in the market. It follows from this that investments can be passive and active:

passive - investments that ensure at least no deterioration in the profitability of investments in the operations of this organization due to the replacement of obsolete equipment, the training of new personnel to replace those who have left, etc.

active - investments that increase the competitiveness of the company and its profitability compared to previous periods through the introduction of new technologies, the release of goods that will be in high demand, the capture of new markets, or the absorption of competing firms.

Investments are divided into the following groups:

By investment objects:

1) real investment is an investment in fixed capital in various forms (acquisition of patents, construction of buildings, structures, investments in scientific developments and etc.);

2) financial (portfolio) investments - this is an investment in shares, bonds and other securities, giving the right to receive income from property, as well as bank deposits.

By the nature of participation in the investment:

1) direct investments are investments made by direct investors, i.e. legal and individuals who fully own the organization or a controlling stake, which gives the right to participate in the management of the organization;

2) indirect investments are investments made through financial intermediaries (investment consultants, financial brokers, brokerage houses, mutual funds, commercial banks, insurance companies).

By investment period:

short-term investments - investments of capital for a period from a week to one year. These investments are, as a rule, speculative in nature. The main task of a short-term investor is to calculate the direction of movement of the paper on a scale of weeks and months, to determine the entry point with the highest ratio of potential income to risk;

medium-term investments - investment of funds for a period of one to five years;

long-term investments - investments of 5 years or more (capital investments in the reproduction of fixed assets).

According to the forms of ownership of investment resources:

state investments - carried out by public authorities and management at the expense of budgets, extra-budgetary funds;

private investments - investments made by individuals or legal entities with the aim of generating income in the future;

combined investments - investments of funds carried out by the subjects of a given country and foreign states in order to obtain a certain income;

foreign investment - the investment of capital by foreign investors in order to make a profit.

By chronological order:

initial investment - aimed at creating an enterprise or building a new facility;

current investments - aimed at maintaining the level of the facility's technical equipment.

For investment purposes:

for the reimbursement of fixed capital;

to expand production;

for the purchase of securities of other organizations;

to innovative technologies.

According to the level of investment risk:

low-risk investments;

medium-risk investments;

high-risk investments.

According to the level of investment attractiveness:

unattractive;

medium attractive;

highly attractive.

Individuals or legal entities that place capital on their own behalf and at their own expense for the purpose of making a profit are called investors.

Investors can invest their own, borrowed and borrowed funds. Investors may be bodies authorized to manage state and municipal property or property rights, legal entities all forms of ownership, international organizations and foreign legal entities, individuals.

Sources of financing of investment activities are:

Own financial resources and intra-economic reserves of the organization (profit, depreciation, cash savings and savings of citizens and legal entities, funds paid by insurance bodies in the form of compensation for losses from accidents, natural disasters, etc.);

Attracted financial resources(received from the sale of shares, share and other contributions of members labor collectives, citizens, legal entities);

Borrowed funds or transferred funds (bank and budget loans, bonded loans, etc.);

Funds from off-budget funds;

Funds from the federal budget provided on a non-refundable basis, funds from the budgets of the constituent entities of the Russian Federation;

Funds from foreign investors.

Investments can be received from one or more sources. Distinguish between centralized (budgetary) - funds of the federal budget, funds of the budgets of the constituent entities of the Russian Federation and local budgets - and decentralized (extrabudgetary) - own funds of enterprises and organizations, foreign investment, attracted funds, funds of extrabudgetary funds - sources of investment.

1.2 Investment attractiveness of the organization and methods of its assessment

The works of many scientists are devoted to the study of the concept of investment attractiveness and methods for its assessment, for example, I.A. Blanca, V.V. Bocharova, E.I. Krylov and others.

Each scientist interprets the concept of investment attractiveness depending on the factors included in its assessment, i.e. there is no single thread. There are many factors affecting investment attractiveness, therefore, in a narrow sense, investment attractiveness is a system or combination of various features or factors of internal and external environment.

The most clearly different points of view on the understanding of investment attractiveness are reflected in Table 2.1.

Table 2.1 - Interpretation of the concept of "investment attractiveness"

	Interpretation of the concept
Blank I.A., Kreinina M.N.	Summarizing the advantages and disadvantages of investing individual directions and objects from the position of a particular investor.
Roizman I.I., Shakhnazarov A.G., Grishina I.V.	A system or combination of various objective features, means, and opportunities that together determine the potential effective demand for investment in a country, region, industry, or enterprise.
Sevryugin Yu.V.	A system of quantitative and qualitative factors that characterizes the solvent demand of an enterprise for investments.
Lyakh P.A., Novikova I.N.	A complex of characteristics of the most profitable and least risky investment of capital in any sphere of the economy or in any type of activity.
Tryasitsina N.Yu.	A set of performance indicators of the enterprise, which determines the most preferred values ​​of investment behavior for the investor.
Group of the Ministry of Economic Development	The volume of investments that can be attracted based on the investment potential of the facility, risks and the state of the external environment.
Putyatina L.M., Vanchugov M.Yu.	An economic category that characterizes the efficiency of using the property of an enterprise, its solvency, financial stability, ability to innovative development on the basis of increasing the return on capital, the technical and economic level of production, the quality and competitiveness of products.
Igolnikov G.L., Patrusheva E.G.	Guaranteed, reliable and timely achievement of the investor's goals based on the economic performance of this invested production.
Guskova T.N., Ryabtsev V.M., Geniatulin V.N.	A certain state of economic development, in which, with a high degree of probability, investments can give a satisfactory level of profit in a timeframe acceptable to the investor, or a positive effect can be achieved by me.
Krylov E.I.	A generalized characteristic in terms of prospects, profitability, efficiency and minimization of the risk of investing in the development of an enterprise at the expense of its own funds and funds of other investors.
Modorskaya G.G.	A complex of economic and psychological indicators of the enterprise's activity, which determine the area of ​​preferred values ​​of investment behavior for the investor.
Bocharov V.V.	The presence of an economic effect (income) from investing money with a minimum level of risk.
Sharp W., Markowitz H.	Getting the maximum profit at a given level of risk.
Eriyazov R.A.	A complex category that includes accounting for internal factors in the form of investment potential, external factors - the investment climate and the contradictory unity of objective and subjective factors in the form of taking into account the level of risk and profitability of investment activity, while the interests of the investor and recipient are consistent.
Latsinnikov V.A.	An indicator of its total value, which is a set of objective (financial condition of the enterprise, its level of development, quality of management, burden of debts) and subjective (ratio of profitability and risk of investments) characteristics necessary to satisfy the interests of all participants in the investment process, allowing to assess the feasibility and prospects of investments and taking into account the combined influence of macro- and meso-environment factors
Nikitina V.A.	The economic feasibility of investing, based on the coordination of the interests and capabilities of the investor and the recipient of investments, which ensures the achievement of the goals of each of them on acceptable level return and risk
Ivanov A.P., Sakharova I.V., Khrustalev E.Yu.	A set of economic and financial indicators of an enterprise that determine the possibility of obtaining maximum profit as a result of capital investment when minimal risk investment of funds.

In this paper, investment attractiveness will be presented as a set of indicators of the organization's performance, which reflect the development of the organization in dynamics, as well as the rational use of available resources.

Investment attractiveness is considered on various levels: at the macro level - the investment attractiveness of the country, the meso level - the investment attractiveness of the region and industry, at the micro level - the investment attractiveness of the organization.

There are a large number of options for assessing investment attractiveness, this is due to the fact that there is no specific definition of the term “investment attractiveness”, from all of them the following methods can be noted, based on the factors put in the assessment methodology:

on the basis of the relationship between profitability and risk (W. Sharp, S.G. Shmatko, V.V. Bocharov) - the establishment of the company's investment risk group. Consequently, an analysis of the risks arising from investment activities is carried out, the significance of the risk is established, and the total investment risk is calculated. Further, the belonging of the organization to a certain category of risk is revealed, on the basis of which investment attractiveness is determined. The key risks considered are: the risk of reduced profits, the risk of loss of liquidity, the risk of increased competition, the risk of changes in the pricing policy of suppliers, etc.

based solely on financial indicators (M.N. Kreinina, V.M. Anshin, A.G. Gilyarovskaya, L.V. Minko) - an analysis of the financial condition is carried out by calculating financial ratios that reflect different aspects of the organization's activities: property status, liquidity, financial stability, business activity and profitability. For the assessment, data from the financial statements of the organization are used.

based on financial economic analysis, in which not only financial, but also production indicators are calculated (V.M. Vlasova, E.I. Krylov, M.G. Egorova, V.A. Moskvitin) - production indicators appear that reflect the availability of fixed assets, the degree of their wear, load level production capacity, availability of resources, number and structure of personnel and other indicators.

on the basis of a comprehensive comparative assessment (G.L. Igolnikov, N.Yu. Milyaev, E.V. Belyaev) - an analysis of indicators of the financial condition, the market position of the organization, the dynamics of development, staff qualifications, and the level of management is carried out. Using this method at the beginning, groups of factors are determined at different levels: countries, regions, organizations, then these groups are selected according to their significance based on expert assessments. The coefficients of significance of each individual factor in the group of factors are also determined, then all factors are summarized taking into account the influence of the significance of each group and the factor in the group. The obtained data are ranked and the most investment-attractive organizations are determined. The factors affecting the investment attractiveness of the country are: the discount rate and its dynamics, inflation rates, technological progress, the state of the country's economy, the level of development of the investment market. The indicators for assessing the investment attractiveness of the region are: production and economic indicators (price index, product profitability, capital productivity, specific gravity all material costs, the number of operating organizations), financial indicators (liquidity ratios, autonomy ratios, etc.), industry production factors (level of production capacity utilization, degree of depreciation of the main production assets), indicators of the investment activity of the industry (the number of investments per organization, the number of investments per employee, the index of the physical volume of investments in fixed assets, etc.).

on the basis of the cost approach, which is based on the determination of the market value of the company and the trend towards its maximization (A.G. Babenko, S.V. Nekhaenko, N.N. Petukhova, N.V. Smirnova) - the undervaluation / overvaluation ratio of the organization is calculated by real investment market as a ratio of different values ​​(real value to market value). The real value is determined as the sum of the value of the property complex and discounted income minus accounts payable. Market price- this is the most possible price for a transaction in a certain period of time, based on market conditions.

These methods are designed for strategic investors whose goal is long-term investment, which involves managing the organization and its operations to achieve specific goals, and most importantly, to increase the value of the organization. Investors who place their investments on short term(speculators) usually use to assess the investment attractiveness of the theory of portfolio investment (a method of forming an investment portfolio aimed at the optimal choice of assets based on the required ratio of profitability / risk), fundamental (price forecasting using the financial performance of the company and calculating the intrinsic value of the company) and technical analyzes(forecasting future value using charts and indicators) .

As the main component of investment attractiveness, financial attractiveness is distinguished, since the organization's finances reflect the main results of its activities. Based on this, the analysis of the investment attractiveness of the analyzed organization will be carried out according to the methodology of financial and economic analysis, namely, on the basis of indicators for assessing the financial condition, which includes:

analysis of the structure and dynamics of property;

analysis of the structure and dynamics of profit;

balance sheet liquidity analysis;

solvency analysis;

creditworthiness analysis;

business activity analysis:

6.1) turnover analysis;

6.2) analysis of return on capital.

analysis of financial stability;

bankruptcy probability analysis.

It will also consider external and internal factors investment attractiveness, such as investment attractiveness of the region and industry, organizational and managerial structure of the organization, coverage of the sales market.

2. ASSESSMENT OF INVESTMENT ATTRACTIVENESS OF SYNTHESIS OF INTELLECTUAL SYSTEMS LLC

2.1 a brief description of organization LLC "SIS"

Synthesis of Intelligent Systems Limited Liability Company refers to IT organizations and specializes in the development of websites and mobile applications. The organization was established in 2015 on the basis of the minutes of the founders' meeting, and is currently located in Tomsk.

The purpose of the creation of Synthesis of Intelligent Systems LLC was to obtain maximum profit at minimum cost by providing services for the development software.

The range of services provided by Synthesis of Intelligent Systems LLC:

website development from scratch on the 1C-Bitrix platform;

website development using a template on the 1C-Bitrix platform;

maintenance of finished sites;

completion and improvement of ready-made sites;

development of mobile applications;

sale of licenses to 1C-Bitrix LLC.

The main clients are legal entities and individual entrepreneurs, there are orders from government agencies.

The analyzed organization according to the current classification can be attributed to small businesses, since. average headcount at the beginning of 2017 it was 17 people, and authorized capital wholly owned by private individuals.

In connection with the non-exceeding of revenue in the amount of 112.5 million rubles for the first nine months of last year, not exceeding average population employees for 2015 in the amount of 100 people, the residual value of fixed assets - 150 million rubles, - the organization applies a simplified taxation system with the object of taxation income minus expenses with an interest rate of 7%, provided for IT-organizations. In accordance with clause 85 "Regulations for maintaining accounting and financial statements in the Russian Federation”, approved by order of the Ministry of Finance of the Russian Federation of July 29, 1998 No. 34n, small enterprises have the right to draw up financial statements in a reduced volume (balance sheet and income statement). SIS LLC applies this right in full.

2.2 Assessment of the investment attractiveness of the organization

investment market sales profit

Analysis of the structure and dynamics of property and sources of its formation

The first stage of the assessment is the vertical (structural) and horizontal (temporal) analysis.

Horizontal analysis is aimed at studying the growth rates of indicators, which explains the reasons for the change in their structure, thus, it represents the absolute and relative change in indicators over the period. Vertical analysis is an analysis of the structure in comparison with the previous period, it helps to understand which indicators had the most significant impact on the indicators.

Analysis of the dynamics and structure of the property of the organization and the sources of its formation is presented in table 3.1.

Table 3.1 - Analysis of the dynamics and structure of the organization's property and sources of its formation

The name of indicators	Absolute values	Relative values	Changes
	2015, thousand rubles	2016, thousand rubles			IN absolute values, thousand roubles.	In structure, %	Rate of increase
Tangible non-current assets
Intangible, financial and other non-current assets
Cash and cash equivalents
Financial and other current assets (including accounts receivable)
Capital and reserves
Long-term borrowings
Other long-term liabilities
Short-term borrowings
Accounts payable
Other current liabilities

Conclusions obtained from the analysis of the asset balance:

The balance sheet asset is dominated by financial and other current assets of the organization, and this case consisting entirely of receivables, which account for 64% of the balance sheet. The shares of other assets are insignificant. Share of material out current assets, namely, fixed assets decreased by 23%, probably due to the depreciation of the main equipment. In absolute terms, fixed assets decreased by 78 thousand rubles, which is probably due to the disposal of fixed assets in the current period. The share of intangible, financial and other non-current assets, namely acquired licenses, decreased by 4%, which indicates the rejection of insignificant software. The share of cash and cash equivalents increased by 5%, in monetary terms by 238 thousand rubles, due to an increase in the volume of services provided. In connection with the increase in volumes, the share of financial and other current assets, represented in this case exclusively by receivables, increased by 22%, which is the provision of deferred payments to customers, as well as the unstable solvency of the main part of buyers.

The growth rate of the balance sheet totaled 131%, which indicates the development of the organization, but since the growth was mainly due to the growth of accounts receivable, although it is an indicator of an increase in the volume of services provided, in general it is a negative indicator - the withdrawal of funds from the turnover of the organization.

Conclusions obtained from the analysis of the sources of property formation:

Accounts payable prevails in the structure of the balance sheet liabilities, amounting to 74%, the growth rate of which amounted to 1192%. The growth of accounts payable shows the inability of the organization to extinguish current liabilities. In the reporting period, the amount of accounts payable amounted to 1,550 thousand rubles. The share of other long-term liabilities, representing loans from the founders, decreased significantly by 36%, in monetary terms by 201 thousand rubles, directly related to the repayment of loans. Short-term borrowings and other short-term liabilities that were necessary when opening an organization were fully repaid by 10% and 2%, respectively, which positively characterizes an organization capable of paying off short-term obligations. The share of long-term borrowings decreased by 12%, which shows that the organization after repayment of short-term obligations, it began to liquidate long-term debts. The share of own funds, which is the authorized capital, has not changed and in monetary terms is 15 thousand rubles. In the overall structure of the balance sheet, the share of own funds is less than 1%, which undoubtedly characterizes the unstable financial position of the organization.

Clearly, the dynamics of the structure of the asset and liabilities of the balance sheet is shown in Figure 3.1.

Figure 3.1 - Dynamics of structural assets and liabilities for 2015-2016

Analysis of the structure and dynamics of performance results

When analyzing performance results, vertical and horizontal analysis is also carried out. The results of the analysis show what indicators profit is formed from, the dynamics of indicators and their impact on the net profit of the organization. An analysis of the dynamics and structure of profit is given in Table 3.2.

Table 3.2. - Analysis of the dynamics and structure of profit

Name indicators			Deviation	revenue in Last year	in % of revenue in the reporting	Deviation
Expenses for ordinary activities
Percentage to be paid
Other income
other expenses
Income taxes (revenues)
Net income (loss)

Conclusion from the analysis: The most significant impact on profit is made by expenses for ordinary activities, which increased in 2016 by 3937 thousand rubles. In 2016, other expenses appeared, the amount of which amounted to 73 thousand rubles. and includes the cost of maintaining a bank account. Revenue in 2016 increased by 4,731 thousand rubles. and amounted to 7535 thousand rubles, which characterizes the development of business. Accordingly, net profit also increased in 2016 by 721 thousand rubles. and amounted to 1100 thousand rubles.

The dynamics of profit indicators is shown in Figure 3.2.

Figure 3.2 - Dynamics of profit indicators

Balance liquidity analysis

The liquidity of an organization is an economic term that refers to the ability of assets to be quickly sold at a price close to the market.

Depending on the degree of liquidity, the assets of the organization are divided into the following groups:

A1 = most liquid assets = cash + short-term financial investments

A2 = marketable assets = accounts receivable

A3 = slow-moving assets = inventories + long-term receivables + VAT + other current assets

A4 = hard-to-sell assets = non-current assets

Liabilities of the balance are grouped according to the degree of urgency of payment:

P1 = most urgent obligations = accounts payable

P2 = short-term liabilities = short-term loans and credits + debts to participants for the payment of income + other short-term liabilities

P3 = long-term liabilities = long-term liabilities + deferred income + reserves for future expenses

P4= permanent \ stable liabilities \u003d capital and reserves

The balance is considered absolutely liquid if the following ratios take place:

A1> P1; A2> P2; A3 > P3; A4< П4.

Comparison of these groups of assets and liabilities is presented in Table 3.3.

Table 3.3 - Comparative analysis of the assets and liabilities of the organization

Based comparative analysis the following conclusions can be drawn:

the organization cannot repay the most urgent obligations with the help of absolutely liquid assets;

the organization cannot repay long-term loans with slow-moving assets;

the organization does not have a high degree of solvency and cannot repay various types of obligations with relevant assets.

Since the ratios are not met, the balance is considered illiquid, i.e. the organization is unable to meet its obligations.

Solvency analysis

The solvency of the organization is the ability of the subject economic activity fully and timely repay their accounts payable. Solvency is one of the key features of sustainable financial regulations organizations.

The solvency of the organization from the position of liquidity of assets is analyzed by means of special financial ratios - liquidity ratios:

general liquidity indicator - shows the organization's ability to pay off its obligations in full with all types of assets;

absolute liquidity ratio; reflects the ability of the organization with the help of highly liquid assets to pay off its short-term obligations. (calculated as the ratio of cash and short-term financial investments to short-term liabilities);

quick liquidity ratio -- shows the possibility of repayment with the help of quickly liquid and highly liquid assets of their short-term liabilities (calculated as the ratio of highly liquid current assets to short-term liabilities);

current liquidity ratio - reflects the organization's ability to pay off its current liabilities with the help of current assets. (calculated as the ratio of current assets to short-term liabilities);

factor of maneuverability of the functioning capital; The maneuverability coefficient shows how much of the functioning capital is immobilized in inventories and long-term receivables;

the share of working capital in the asset - characterizes the presence of working capital in the assets of the organization;

the coefficient of security with own funds - reflects the degree of use by the organization of its own working capital; shows the share of the company's current assets financed from the organization's own funds.

The calculation of solvency indicators is presented in table 3.4.

Table 3.4 - Analysis of the solvency of the organization

Indicators	Symbol		Indicator value	Change
General indicator liquidity		(A1+0.5A2+0.3A3)/(P1+0.5P2+0.3P3);
Absolute liquidity ratio
Quick liquidity ratio		(A1 + A2) / (P1 + P2)
Current liquidity ratio		(A1 + A2 + A3) / (P1 + P2)
Operating capital maneuverability ratio		A3 / ((A1 + A2 + A3) - (P1 + P2))	decrease in the indicator
Share of working capital in assets		(А1+А2+А3) / Total balance
Equity ratio		(P4 - A4) / (A1 + A2 + A3)

Conclusion from the analysis: The overall liquidity ratio in 2016 decreased and amounted to 0.59, which shows that the liquidity level of the organization is not optimal. The absolute liquidity ratio decreased by 0.32 and amounted to 0.16, which indicates that the amount of cash can cover only 16% of the company's liabilities, which is not enough to maintain a normal level of liquidity of the organization. The quick liquidity ratio amounted to 1.07, which is slightly higher than the norm and indicates the possibility of quick repayment of debts in the medium term. This means that SIS LLC is able to withdraw funds from circulation and pay off short-term obligations at an average speed. The current liquidity ratio was 1.07 in 2016, which indicates a low solvency. The coefficient of maneuverability of the functional has a zero value due to the lack of slow-moving assets in the organization. The share of working capital increased by 0.27 and amounted to 0.8, which is a positive factor, showing an increase in the liquidity of the balance sheet. The security ratio has a negative value, but it is positive in dynamics, in 2016 it was -0.25, which shows that current assets are financed by borrowed funds of the organization, since the coefficient value is less than 0.1 and the current liquidity ratio is less than 2, then the organization is insolvent.

Creditworthiness analysis

The concept of solvency of the organization is closely related to creditworthiness. Creditworthiness reflects, to a greater extent, the repayment of obligations with the help of medium-term and short-term assets of the organization, excluding fixed assets.

The main solvency indicators are:

ratio of sales volume to net current assets;

Net current assets are current assets minus short-term debts of the organization. The ratio of the volume of sales to net current assets shows the efficiency of the use of current assets.

ratio of sales volume to equity capital;

the ratio of short-term debt to equity;

the ratio of receivables to sales revenue.

The calculation of creditworthiness indicators is presented in table 3.5.

Table 3.5 - Analysis of creditworthiness indicators

Indicators			Absolute deviation
Current assets, thousand rubles
Short-term borrowed funds thous.
Revenue thousand rubles
Equity capital thousand rubles.
Accounts receivable thousand rubles
Net current assets thous. rub.
Indicators:
The ratio of sales volume to net current assets
The ratio of sales volume to equity
The ratio of short-term debt to equity
The ratio of receivables to sales revenue

Based on the analysis, we can draw the following conclusions: The efficiency ratio of the use of current assets in 2016 compared to 2015 increased by 53.92, which shows the efficiency of the use of current assets. The ratio of sales volume to equity was 502.33, which was the result of a sharp increase in revenue. The ratio of short-term debt to equity increased by 88.53 and amounted to 103.33, which indicates a high share of short-term debt in equity and the inability of the organization to pay off its obligations. The ratio of receivables to sales increased by 0.04 to 0.18, which can be seen as a sign of deteriorating creditworthiness as buyers' debts are more slowly monetized.

Analysis of business activity indicators

The next step is to analyze business activity indicators.

Analysis of business activity allows to draw a conclusion about the effectiveness of the organization. Indicators of business activity are related to the rate of turnover of funds: the faster the turnover, the less semi-fixed costs per turnover, which means the higher the financial efficiency of the organization.

Analysis of business activity, as a rule, is carried out at two levels: qualitative (breadth of sales markets, business reputation of the organization and its customers, competitiveness, etc.) and quantitative indicators. At the same time, the analysis of quantitative indicators consists of two stages: analysis of turnover (own capital, current assets, receivables and payables) and profitability.

Asset turnover analysis

Key turnover indicators include:

return on equity ratio - shows how much rub. revenue falls on 1 rub. average amount of invested own capital;

capital productivity of fixed assets - characterizes the amount of proceeds from the sale attributable to the ruble of fixed assets;

return ratio of intangible assets - reflects the effectiveness of the use of intangible assets. It shows the amount of sales revenue in rubles per 1 ruble of the average amount of intangible assets, as well as the number of turnovers for the period;

total asset turnover ratio - shows how many monetary units of sold products each monetary unit of assets brought;

turnover ratio of current assets (current assets) - reflects the efficiency of the use of current assets. It shows the amount of sales revenue in rubles per 1 ruble of the average amount of current assets, as well as the number of turnovers for the period;

cash turnover ratio - shows the period of cash turnover;

inventory turnover ratio - shows how many times during the study period the organization used the average available balance of stocks;

accounts receivable turnover ratio - shows the number of payments received from buyers for a period in the amount of the average cost of accounts receivable. The maturity of receivables - shows how many days on average the receivables of the organization are repaid;

accounts payable turnover ratio - shows how many times the company repaid the average value of its accounts payable. The maturity of accounts payable - shows the average period of repayment of the organization's debts for current liabilities;

the operating cycle reflects the period of time from the moment the materials arrive at the warehouse until the moment when the buyer receives payment for the products;

The financial cycle shows the length of time from the moment of payment for materials to suppliers and ending with the receipt of money from buyers for the delivered products.

The calculation of turnover rates is presented in table 3.6.

Table 3.6 - Turnover analysis

Indicators	Conditional designation	Calculation algorithm			Change
Continuation of table 3.6
Number of days in the reporting year
Average cost of own capital, thousand rubles		(SKng+SKkg)/2
Average cost of fixed assets, thousand rubles		(OSNG+OSCG)/2
Average cost of intangible assets, thousand rubles		(Nmang+Nmakg)/2
Average accounts payable debt, thousand rubles		(KZng+KZkg)/2
average cost assets, thousand rubles		(Ang+Akg)/2
Average cost of current assets, thousand rubles		(Aobng+ Aobkg)/2
Including:
Cash, thousand rubles		(DSng+DSkg)/2
Reserves, thousand rubles		(Zng+Zkg)/2
Accounts receivable, thousand rubles		(DZng+DZkg)/2
Estimated coefficients:
Return on equity ratio
return on assets
Return ratio of intangible assets
Coefficient asset turnover
Coefficient turnover of current assets
Coefficient inventory turnover
Coefficient accounts payable turnover
Turnaround time, days:
current assets
Money
Accounts receivable
accounts payable		D/kobred
Duration operating cycle		ext. zap + ext. Deb
Duration financial cycle		D. pr.c. + Add.deb-Add. Creed

Based on the data, the following conclusions can be drawn: The total asset turnover ratio in 2016 compared to 2015 decreased by 1.18, which shows a decrease in the efficiency of using all available resources, regardless of their sources of financing (for each ruble of assets, there are 5.04 ruble of sold products). The turnover ratio of working capital in 2016 decreased by 4.75, which indicates a decrease in the efficiency of the use of current assets in the organization (for each ruble of current assets, there are 7.04 rubles of sold products). The return ratio of intangible assets increased by 0.64, which shows the effectiveness of the use of intangible assets (49.41 rubles of sold products account for each ruble of current assets). Return on assets in 2016 increased by 9.63, which is proof best use fixed production assets (for every ruble of current assets, there are 27.60 rubles of sold products). The return on equity increased by 128.47, which was achieved through an increase in sales revenue, also due to the large share of profits received through the use of borrowed funds, in the long term, may adversely affect financial stability. The inventory turnover ratio is not calculated due to their absence. The cash turnover ratio increased by 4 days, which indicates the rational organization of the company's work. The receivables turnover ratio decreased by 6.07 and, accordingly, the turnover period increased by 17 days, which indicates a slower repayment of receivables. The accounts payable turnover ratio decreased by 37.71 and, accordingly, the turnover period increased by 33 days, which indicates a slowdown in the repayment of accounts payable.

The duration of the operating cycle increased by 17 days, which is associated with an increase in the period of receivables turnover, i.e. the number of days required for the transformation of raw materials and materials into cash became 41 days.

The duration of the financial cycle decreased by 16 days, due to the increase in the duration of the period of turnover of receivables and payables, i.e. the number of days between the repayment of accounts payable and accounts receivable is 1 day.

Profitability analysis

In the broad sense of the word, the concept of profitability means profitability, profitability. An organization is considered profitable if the results from the sale of products cover production costs and, in addition, form an amount of profit sufficient for the normal functioning of the organization.

The economic essence of profitability can be disclosed only through the characteristics of the system of indicators. Their general meaning is in determining the amount of profit from one ruble of invested capital.

The main indicators of profitability are:

return on assets (economic profitability) - shows the amount of net profit attributable to each monetary unit invested in the company's assets, reflects the efficiency of using the organization's assets.

2) return on equity - shows the amount of net profit for each cost unit of capital owned by the owners of the company.

3) return on sales - shows the amount of the organization's net profit from each ruble of products sold.

4) profitability of production - shows the amount of the organization's profit from each ruble spent on the production and sale of products.

5) return on invested capital - shows the ratio of profit to investments aimed at obtaining this profit. Investments are considered as the sum of own capital and long-term borrowed funds.

Calculation of indicators of profitability of capital is presented in table 3.7.

Table 3.7 - Analysis of return on equity

Indicators	Conditional designation	Calculation algorithm			Absolute change
Revenue (net) from the sale of goods, products, works, services, thousand rubles.
Cost of sales of goods, products, works, services (including commercial and administrative expenses), thousand rubles
Profit from sales, thousand rubles
Net profit, thousand rubles
Asset value, thousand rubles		(Ang+Akg)/2
Own capital, thousand rubles		(Skng+SKkg)/2
Long-term liabilities, thousand rubles		(Dong+Docg)/2
Profitability indicators:
Return on assets
Return on equity
Return on invested capital		PR/ (sk+to)
Profitability of sales
Profitability of production

Return on sales in 2016 amounted to 0.15, i.e. each ruble of revenue received contained 15 kopecks of net profit, this indicator increased by 0.01, which indicates a slight increase in demand for the services provided. The profitability of production in 2016 amounted to 0.18, i.e. each ruble spent on the provision of services began to bring a net profit of 18 kopecks. Return on assets in 2016 decreased by 0.1 and amounted to 0.74, i.e. each ruble of assets began to generate a profit of 74 kopecks. The return on equity increased by 23.47 and amounted to 74, which is associated with an increase in profits and an increase in borrowed capital. The return on invested capital increased by 0.7 and amounted to 1.87, i.e. each ruble of investment began to generate a profit of 1.87 rubles.

Financial stability analysis

Financial sustainability is the ability of an organization to maintain its existence and smooth operation, due to the presence of certain free funds and balance of financial flows. Financial stability means that the organization will be solvent in the long run.

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Approaches to assessing the investment attractiveness of an enterprise. State chemical industry Russia. General characteristics of the enterprise CJSC Sibur-Khimprom. Project risk assessment. Analysis of the dynamics of the composition and structure of sources of property formation.

thesis, added 03/15/2014


The main methods for assessing the investment attractiveness of a municipality used in Russia and abroad. Situational analysis of the Tarnogsky municipal district, assessment of its investment attractiveness, ways and means of its improvement.

thesis, added 11/09/2016


The concept, monitoring and methodological approaches to the analysis of the investment attractiveness of the enterprise. Characteristic, the financial analysis and analysis of the investment attractiveness of OAO "Lukoil". Ways to increase the investment attractiveness of the enterprise.

term paper, added 05/28/2010


Assessment of investment attractiveness of companies. Analysis of the system of indicators of the investment attractiveness of the issuing organization and their significance for making decisions regarding investment. Types of investor goals when investing in financial assets.

test, added 06/21/2012


Organizational and economic characteristics of the modern Russian enterprise. Analysis of the financial condition of the organization. Enterprise risk management in the system of increasing investment attractiveness. Grade economic activity companies.

thesis, added 05/25/2015


Economic essence and financial potential of the enterprise, methodology for its assessment. The relationship of the financial and investment attractiveness of the organization. Analysis of the property status of OJSC "Neftekamskneftekhim" and directions for improving its activities.

thesis, added 11/24/2010


Methodological approaches to the analysis of investment attractiveness and the factors that determine it. Algorithm for monitoring the investment attractiveness of an enterprise. Analysis of liquidity and solvency on the example of the enterprise OAO "Lukoil".

term paper, added 04/14/2015


The essence and criteria of investment attractiveness. The role of investments in the socio-economic development of the municipality. Problems and prospects for the development of the investment attractiveness of the municipality on the example of the city of Krasnodar.

Artificial intelligence(English - artificial intelligence) - these are artificial software systems, created by man on the basis of a computer and imitating the solution of complex creative tasks by a man in the course of his life. According to another similar definition, "artificial intelligence" is a computer program with the help of which a machine acquires the ability to solve non-trivial problems and ask non-trivial questions.

There are two areas of work that make up artificial intelligence (AI). The first of these directions, which can be conditionally called bionic, aims to simulate the activity of the brain, its psychophysiological properties, in order to try to reproduce artificial intelligence (intelligence) on a computer or with the help of special technical devices. The second (main) line of work in the field of AI, sometimes called pragmatic, associated with the creation of systems for the automatic solution of complex (creative) problems on a computer without regard for the nature of the processes that occur in the human mind when solving these problems. Comparison in this case is carried out according to the effectiveness of the result, the quality of the solutions obtained.

1) Exists target, i.e. That final result, to which human thought processes are directed (“The goal makes a person think”).

2) The human brain stores a huge number facts And rules their use. To achieve a certain goal, it is only necessary to turn to the necessary facts and rules.

3) Decision-making is always carried out on the basis of ad hoc simplification mechanism, which allows to discard unnecessary (insignificant) facts and rules that are not related to the task being solved at the moment, and, conversely, to highlight the main, most significant facts and rules needed to achieve the goal.

4) Achieving the goal, a person not only comes to the solution of the task assigned to him, but at the same time acquires new knowledge.

Building a universal AI system covering all subject areas is impossible, as this would require an infinite number of facts and rules. More realistic is the task of creating such AI systems that are designed to solve problems in a narrowly defined, specific problem area.

Rice. 5.1. AI System Components

Such systems, using the experience and practical knowledge of experts in a given subject area, are called expert systems(expert systems).

The use of expert systems is extremely effective in various areas of human activity (medicine, geology, electronics, petrochemistry, space research, etc.). This is due to a number of reasons: firstly, it becomes possible to solve previously inaccessible, poorly formalized problems using a new mathematical apparatus specially developed for these purposes (semantic networks, frames, fuzzy logic, etc.); secondly, the created expert systems are focused on their operation by a wide range of specialists (end users), communication with which takes place in an interactive mode, using the reasoning technique and terminology of a specific subject area that they understand; thirdly, the use of an expert system makes it possible to dramatically increase the efficiency of decisions made by ordinary users due to the accumulation of knowledge in the expert system, including the knowledge of highly qualified experts.

The expert system includes a knowledge base and subsystems: communication, explanation, decision making, knowledge accumulation. Through the subsystem of communication with the expert system are connected: the end user; expert - a highly qualified specialist whose experience and knowledge far exceed the knowledge and experience of an ordinary user; a knowledge engineer who is familiar with the principles of building an expert system and knows how to work with experts in this field, who knows special languages ​​for describing knowledge.

Control systems built on the basis of expert controllers that imitate the actions of a human operator under conditions of uncertainty in the characteristics of an object and the environment are called intellectual control systems (intelligent control systems).

According to another similar definition, intellectual A control system (MCS) is one that has the ability to understand, reason, and study processes, disturbances, and operating conditions. The factors under study here are mainly process characteristics (static and dynamic behavior, disturbance characteristics, equipment operating practices). It is desirable that the system itself accumulate this knowledge, purposefully using it to improve its qualitative characteristics.

1

The paper considers the problem of synthesizing an intelligent multi-purpose control system. For a given mathematical model of the control object, control objectives, quality criteria, restrictions, it is necessary to find a control that ensures the achievement of several goals and minimizes the value of the quality criterion. Goals of control are given in the form of points in the state space that must be achieved in the control process. A feature of the problem is that the control is sought in the form of two multidimensional heterogeneous functions of the coordinates of the state space. One function ensures that the object reaches private purpose, and another function, logical, provides switching of private targets. To solve the multipurpose control synthesis problem, the network operator method is used. When solving the main synthesis problem, together with the synthesizing functions for each subtask, we determine the choice function, which provides control switching from solving one subtask to solving the next subtask.

network operator.

intelligent control

1. Diveev A. I., Sofronova E. A. The network operator method and its application in control problems. Moscow: RUDN University, 2012. 182 p.

2. Diveev A. I. Synthesis of an adaptive control system by the network operator method // Questions of the theory of security and stability of systems: Sat. articles. M.: VTS RAS, 2010. Issue. 12. S. 41-55.

3. Diveev A. I., Sofronova E. A. Identification of the inference system by the network operator method // Vestnik RUDN University. Series Engineering Research. 2010. No. 4. S. 51-58.

4. A. I. Diveev and N. A. Severtsev, “Network operator method for designing a spacecraft descent control system under uncertain initial conditions,” Problemy mashinostroeniya i nadezhnosti mashin. 2009. No. 3. S. 85-91.

5. A. I. Diveev, N. A. Severtsev, and E. A. Sofronova, “Synthesis of a control system for a meteorological rocket using genetic programming,” Problemy mashinostroeniya i nadezhnosti mashin. 2008. No. 5. S. 104 - 108.

6. Diveev A. I., Shmalko E. Yu. Multi-criteria structural-parametric synthesis of a spacecraft descent control system based on the network operator method. Vestnik RUDN University. Engineering Research Series ( information Technology and management). 2008. No. 4. S. 86 - 93.

7. Diveyev A. I., Sofronova E. A. Application of network operator method for synthesis of optimal structure and parameters of automatic control system// Proceedings of 17th IFAC World Congress, Seoul, 2008, 05.07.2008 – 12.07.2008. P. 6106 - 6113.

Consider the problem of synthesizing a control system with several control objectives.

A system of ordinary differential equations is given that describes the model of the control object

where , , is a bounded closed set, .

The state of the control object is estimated by the observed coordinates

System (1) is given initial conditions

Set of target states

, (4)

The criterion of control quality is set

, (5)

where is the control time, which can be limited, but not specified.

Need to find a control in the form

which provides successive achievement of all target points (4) and minimizes the functional (5).

The goal of control (4) is multivalued. To proceed to the task of synthesizing an intelligent control system, it is necessary to provide a choice in the system. For this purpose, we weaken the requirements for the object to hit each target point, and replace it with the requirement to hit the target point in the neighborhood.

Then we have a compromise between accuracy and speed of reaching the target points. For implementations of control in this problem, we need to solve the problem of choosing between the exact achievement of the current goal and the transition to another goal each time. It is obvious that under such a condition, in addition to the feedback controller that ensures the achievement of the goal, it is necessary to have a logical block in the control system that switches the goals.

Let us refine this statement of the problem.

We represent control (6) as a function depending on the distance to the target

(8)

where is the number of the current target point.

At any point in time, the number of the current target point is determined using the logical function

, , (9)

Where , , - predicate function,

: . (10)

Function (10) must also be found together with synthesizing function (6). Function (10) should provide switching of target points. Both functions (6) and (10) must provide a minimum for the quality functional (5) for the accuracy functional

, (11)

Control time is determined by reaching the last target point

If , (12)

where is a small positive value.

Partial criterion (5) will be replaced by the total quality criterion

(13)

To construct a predicate function, we use the discretization function and the logical function.

, (14)

where is a logical function,

: , (15)

Where , , - discretization function.

The task is to find controls in the form

where is an integer vector that determines the controls for solving a particular problem . Control (16) must ensure that the minimums of functionals (11) and (13) are achieved.

In the general case, since the problem contains two criteria (11) and (13), then its solution will be the Pareto set in the space of functionals . A specific solution for the Pareto set is chosen by the developer based on the results of modeling and research of the synthesized control system.

Problem (1) - (3), (7) - (16) is called the problem of synthesis of an intelligent control system. To solve it, it is necessary to find two multidimensional synthesizing functions and .

To solve the problem of synthesis of an intelligent control system, we use the network operator method. To find a function, we use the usual arithmetic network operator, in which we use a set of arithmetic functions with one or two arguments as constructive functions. In the network operator method, these functions are called unary or binary operations. To find the logical function, we use the logical network operator, respectively, with unary and binary logical operations.

As an example, consider the following mathematical model

where , - coordinates on the plane.

Management is limited

The motion trajectory is given by a set of points.

It is necessary to find a control to minimize the two objective functions of the object. The first functional determines the accuracy of movement along the trajectory, and the second - the time of passage of the trajectory.

480 rub. | 150 UAH | $7.5 ", MOUSEOFF, FGCOLOR, "#FFFFCC",BGCOLOR, "#393939");" onMouseOut="return nd();"> Thesis - 480 rubles, shipping 10 minutes 24 hours a day, seven days a week and holidays

Sitnikov Mikhail Sergeevich. Analysis and synthesis of intelligent automatic control systems with fuzzy controllers: dissertation... candidate of technical sciences: 05.13.01 / Sitnikov Mikhail Sergeevich; [Place of protection: Mosk. state in-t of radio engineering, electronics and automation].- Moscow, 2008.- 227 p.: ill. RSL OD, 61 08-5/1454

Introduction

CHAPTER 1. Applications and research methods for intelligent automatic control systems with fuzzy controllers 14

1.1. Overview of ISAS applications with HP 14

1.2. Problems of ISAU research with HP 24

1.3. Investigation of the influence of the main HP parameters on the nature of nonlinear transformations 28

1.3.1 Influence of the form and relative placement of membership functions of individual terms on the nature of nonlinear transformations in the Mamdani fuzzy model 35

1.3.2 Influence of the order of interrelations of input and output terms on the nature of nonlinear transformations in the Mamdani fuzzy model 41

1.4. Chapter 43 Conclusions

CHAPTER 2. Analysis and synthesis of intelligent automatic control systems based on the harmonic balance method 45

2.1. Study of ISAU by the method of harmonic balance 46

2.2. Indirect quality assessment 73

2.3. Influence of fuzzy controller parameters on EKKU 81

2.4. Methods of research and synthesis of ISAU with HP based on the method

harmonic balance 90

2.5. Chapter 98 Conclusions

CHAPTER 3. Research of intelligent automatic control systems based on absolute stability criteria 99

3.1. ISAU absolute stability study with HP 99

3.2. Study of the absolute stability of ACS with several nonlinearities, 100

3.3. Investigation of the absolute stability of the ISAU equilibrium position with a fuzzy controller of the first type 105

3.4. Study of the absolute stability of processes in ISAS with a fuzzy controller of the first type; 119

3.5. Study of the influence of fuzzy controller parameters on the absolute stability of ISAS ". 124

3.6. Indirect assessments of the quality of ISAS regulation based on the criterion of absolute process stability 137

3.7. Chapter 139 Conclusions

CHAPTER 4 Automated synthesis of fuzzy controllers based on genetic algorithms 141

4.1. Overview of automated synthesis methods 141

4.2. Using genetic algorithms to solve problems of automation of synthesis and tuning of fuzzy controllers 144

4.3. Algorithms for synthesis of ISAU with HP 151

4.4. Automated Synthesis and HP 155 Tuning Technique

4.5. Chapter 167 Conclusions

CHAPTER 5. Software and hardware implementation of analysis and synthesis methods for intelligent automatic control systems with fuzzy controllers 169

5.1. Software complex for analysis and synthesis of ISAU with HP 170

5.2. Hardware implementation of the electric drive control system 177

5.3. Synthesis of HP ISAU for DC motor 180

5.4. Experimental studies 190

5.5. Chapter 199 Conclusions

References 203

Appendix 211

Introduction to work

The use of intelligent technologies provides a solution to a wide range of adaptive control problems under conditions of uncertainty. At the same time, the software and hardware of such systems turn out to be simple and reliable, guarantee high quality management. The openness of such technologies allows the integration of event forecasting mechanisms, generalization of accumulated experience, self-learning and self-diagnosis algorithms, thereby significantly expanding the range functionality intelligent systems. The presence of a clear human-machine interface gives intellectual systems fundamentally new qualities that can significantly simplify the stages of training and setting tasks.

One of the common intellectual technologies that has been widely used and has proven itself as a convenient and powerful mathematical tool is the fuzzy logic (FL) apparatus. The theory of fuzzy sets and the logic based on it make it possible to describe inaccurate categories, representations and knowledge, operate with them and draw appropriate conclusions and conclusions. The presence of such opportunities for the formation of models of various objects, processes and phenomena at a qualitative, conceptual level determined the interest in the organization of intelligent control based on the use of this apparatus.

The results of theoretical and experimental studies show that the use of NL technology makes it possible to create highly efficient high-speed controllers for a wide class of technical systems used in industrial, military and household appliances, which have a high degree of adaptability, reliability and quality of operation under conditions of random disturbances, uncertainty of the external load.

Today, this apparatus is considered one of the promising tools for describing particular and non-standard cases that arise during the functioning of the system. The peculiarity of the “fuzzy” representation of knowledge, as well as the unlimited number of input and output variables and the number of embedded rules for the system behavior, allow using this technology to form almost any control law, i.e. to build a new type of non-linear controller, which distinguishes NL technology from others.

The regulator implemented on this technology will be called fuzzy (HP). In the general case, HP is a frequency-dependent and nonlinear converter, which naturally causes a number of problems associated with the study of the stability and quality of control of intelligent automatic control systems (ICAS) with such controllers.

Most topical issues that require a solution and provide a wider use of HP in engineering practice are:

Study of the features of the nonlinear transformation in HP;

Development of engineering methods for studying the stability and quality of management of ISAS with HP;

Development of techniques for tuning and synthesis of HP;

Creation of a toolkit to automate the HP configuration process.

The subject of research is nonlinear transformations implemented in HP, dynamic processes in ISAS with HP, stability and quality of control of intelligent automatic control systems.

The object of research is intelligent automatic control systems with fuzzy controllers.

Goal of the work

Development of algorithmic, software and hardware tools for the study and synthesis of high-quality ISAS with HP. To achieve this goal, it is necessary to solve the following tasks:

1. Investigate the features of the influence of HP parameters: number, type of membership functions (FP) and base of production rules (BP) on the nature of the nonlinear transformation carried out by it.

2. Based on the methods known in TAU, develop mathematical models and appropriate engineering techniques for the study of periodic processes, absolute stability and quality of ISAS with HP.

3. Develop methods for synthesizing HP parameters according to the given quality indicators of ISAS.

4. Develop an algorithm for automated synthesis and tuning of HP parameters to ensure stability and the required quality indicators of ISAS.

5. Develop a software and hardware complex for designing ISAS with HP.

The research methods in this work are based on the theory of automatic control, the theory of nonlinear systems, methods of mathematical and simulation modeling, graphic-analytical methods for solving problems, the theory of fuzzy logic, the theory of optimization and the theory of genetic algorithms.

The validity and reliability of scientific provisions, conclusions and recommendations is confirmed by theoretical calculations, as well as the results numerical simulation and results of experimental studies. The results of modeling in the Matlab environment, experimental studies of the control system in the Simulink environment and on the ISAS hardware and software design complex fully confirm the theoretical provisions and recommendations of the dissertation work and allow them to be used in the design of real ISAS. Basic provisions for defense

1. Results of studying the features of the influence of HP parameters (number, type of FP and BP) on the nature of its nonlinear transformations.

2. Mathematical model for the study of periodic oscillations and control quality in ISAS with HP based on the harmonic balance method.

3. Criteria for the absolute stability of processes and the equilibrium position of ISAU with HP.

4. Engineering methods for the study of periodic oscillations, indirect assessment of the quality of control and absolute stability of ISAS with HP.

5. Technique for synthesis of HP ISAS with a given control quality.

6. Algorithm for automated synthesis and tuning of HP parameters using genetic algorithms.

7. Software and hardware complex for designing ISAS with HP. Scientific novelty

1. Dependences of the characteristics of the nonlinear transformation HP on the parameters of fuzzy calculations (the type and location of membership functions, the base of production rules) are substantiated.

2. Mathematical models have been developed that allow using the harmonic balance method to investigate periodic fluctuations and the quality of ISAS control.

3. Criteria of absolute stability of processes and equilibrium position in ISAS with HP have been developed.

4. On the basis of genetic algorithms, the problem of automated synthesis and tuning of HP parameters was solved, taking into account the required quality of ISAS control.

Practical value

1. Convenient engineering methods have been developed for studying periodic oscillations and indirectly assessing the quality of control of ISAS with HP based on the harmonic balance method.

2. Convenient engineering methods have been developed for studying the absolute stability of processes and the equilibrium position in ISAS with HP.

3. A technique for automated synthesis and tuning of HP parameters has been developed, taking into account the areas of stability and quality of ISAS.

4. A software and hardware complex for research and design of ISAS with HP has been created.

5. The results of the dissertation work were used in the research work "Latilus-2" carried out on the instructions of the SPP at the Presidium of the Russian Academy of Sciences, "Exploratory research and development of intelligent methods for precision control of actuators of promising weapons and military equipment." In particular - It is shown that the use of HP, which implement a nonlinear control law, can significantly improve the quality of control of the executive drives of new types of weapons and military equipment (speed increases by 2-3 times, overshoot decreases by 20%). The control error from the impact of the load can be reduced several times.

Convenient graphic-analytical methods for the analysis and synthesis of ISAS with HP for actuating drives and promising samples of weapons and military equipment are proposed.

6. The results of the dissertation work were used in the performance of work on grants from the RFBR:

2005-2006, project number 05-08-33554-a "Development of mathematical models and methods of harmonic balance for the study of periodic processes and control quality in fuzzy systems."

2008-2010, project number 08-08-00343-a "Automated synthesis of fuzzy controllers based on genetic algorithms".

Approbation of work. The main provisions of the work were discussed and reported at the conference on robotics in memory of academician E.P. Popov (Moscow State Technical University named after N.E. Bauman, 2008), at the XIV and XV international scientific and technical seminars "Modern technologies in the problems of control, automation and information processing" (Alushta 2006-2007), at the XV International student school - seminar "New information technologies" (Sudak 2006), at the I All-Russian scientific conference students and graduate students "Robotics, mechatronics and intelligent systems" (Taganrog, 2005), at the All-Russian competition of scientific and technical creativity of students of higher educational institutions"EUREKA-2005" (Novocherkassk, 2005), at the scientific-practical conference "Modern information technologies" in management and education. (Sunrise) Moscow 2006

Publications

The main results of the dissertation work were published in 8 printed works, including one article in a journal from the VAK list and one monograph.

In the first chapter, based on a review of the areas of application of systems with HP, their wide use in various fields of science and technology is shown. A number of advantages are shown, among which are high quality control, efficiency and functionality.

At the same time, it is shown that today there are no methods and techniques convenient for engineering practice that allow carrying out a full cycle of analysis and synthesis of ISCS with HP.

In the chapter, the features of the influence of HP parameters (number, type of FP and BP) on the nature of its non-linear transformation between input and output signals are studied. The conducted studies, on the one hand, are the necessary basis for the adequate application of the methods of studying nonlinear systems to the study of ISAE with HP and, in particular, the method of harmonic balance and absolute stability criteria, and on the other hand, the solution of the problem of synthesis of ISAE with given properties is possible only when understanding the dependence of the non-linear transformation on the HP settings.

On the basis of the conducted research, the tasks of the dissertation work are substantiated.

In the second chapter, mathematical models have been developed that allow using the method of harmonic balance to investigate periodic oscillations in ISAS with HP. Also, the possibility of an indirect assessment of the quality of ISAS with HP based on the method of harmonic balance in terms of oscillation is substantiated, and an appropriate technique has been developed.

The problem of synthesis of ISCS with HP with given quality indicators based on the harmonic balance method is solved.

The chapter investigates and shows the influence of the form of membership functions and the relative placement of terms, as well as the influence of production rules on the nature of the ECG HP.

The results of experimental studies on computer models confirmed the adequacy of the developed methods for the analysis and synthesis of ISAS with HP based on the harmonic balance method.

In the third chapter, mathematical models are developed that make it possible to transform the structure of the ISAS with HP of the first type to the structure of a nonlinear multiloop ACS. Taking into account the nature of the nonlinear transformations HP, on the basis of the criteria for absolute stability of processes and the equilibrium position for systems with several nonlinearities, the corresponding criteria for ISAS with HP of the first type have been developed.

Based on the proposed criteria, a graphical-analytical method for studying the stability of the equilibrium position and processes in ISAS with HP has been developed.

To solve the problems of synthesis of ISAS, a study was made of the dependence of the regions of absolute stability of ISAS on the HP parameters (the type and number of FPs and BPs).

On the basis of the criterion of absolute stability of processes, a method for indirect assessment of the quality of ISAS with HP has been developed.

Studies were carried out on computer models, the results of which confirmed the adequacy of the developed methods for studying the absolute stability of the equilibrium position and processes in ISAS with HP.

The fourth chapter is devoted to the development of algorithms and methods for automated synthesis of HP parameters in ISAS. The analysis carried out in the dissertation showed that genetic algorithms (GA) are by far the most promising technology for solving this problem. When developing an automated synthesis algorithm, the following tasks were solved: synthesis of an ISAS simulation model; selection of initial HP parameters and GA search parameters; assessing the quality of ISAU management; chromosome coding. The example shows the performance of the automated synthesis algorithm.

The fifth chapter checks the theoretical results obtained in chapters 2-4. A software and hardware complex is being developed that allows carrying out a full cycle of designing fuzzy controllers, starting with the development of mathematical models and ending with direct testing on real equipment. The chapter developed and presented software package for the analysis and synthesis of ISAU models with HP. The structure of the interaction between the software and hardware (bench) parts of the complex is implemented, which makes it possible to conduct full-scale experiments on controlling a DC motor at various types loads and disturbances

The chapter presents the results of experimental studies, including automated synthesis of HP parameters, with verification on a real stand, as well as a comparative assessment of the results of tuning the quality of control of an automatically tuned ISAS with HP and an ACS with a PID controller tuned by the method of inverse problems of dynamics (OZD).

In conclusion, the main scientific and practical results of the dissertation work are presented.

Investigation of the influence of the main HP parameters on the nature of nonlinear transformations

Despite the sufficient prevalence and popularity, the use of the NL apparatus is associated with significant difficulties. First of all, this is due to the lack of complete engineering tools for analyzing the quality of the functioning of fuzzy systems, as well as studying their stability.

Against the background of the lack of effective methods for analyzing fuzzy systems, the problem of HP synthesis becomes even more acute, since the dependence of the influence of its parameters on the quality of ISAS work has been studied rather poorly. These factors largely hinder the wider introduction of HP into the practice of creating new ACS.

The first Lyapunov method makes it possible to analyze the quality of control using linearized ACS equations and can be applied to systems of any structure. This method makes it possible to obtain the necessary conditions for the stability of the system in the small, but for large deviations of the system it does not guarantee stability. It requires linearization of non-linear elements included in the ACS, therefore it is suitable only for the analysis of ACS with primitive fuzzy calculations.

The second Lyapunov method allows one to obtain sufficient stability conditions. It is assumed that ISAE with a fuzzy controller is described by a system of nonlinear differential equations of the first order, and on this basis, taking into account the specifics of the nonlinear transformation, a special Lyapunov function is constructed, the properties of which allow us to analyze the stability of the system under study and determine some quality indicators. The problems of applying this method include the difficulty of choosing a function corresponding to the system, which also includes the representation of fuzzy calculations. Some of the first work, in relation to specific systems with HP are.

As a note, it should be noted that among the NV algorithms (Mamdani, Tsukamoto, Takagi-Sugeno (T-S), Larsen) Mamdani and Takagi-Sygeno are considered the most common in application. To study ISAU with HP built according to T-S algorithm, developed the analytical method of the same name for studying the stability of Takagi-Sygeno, based on the second Lyapunov method. This method does not apply to systems with NV built according to the Mamdani algorithm.

The approximate method of harmonic balance based on the filter hypothesis makes it possible to study self-oscillations in a fuzzy system. This method is graphic-analytical and allows you to study ISAU without representing HP in an analytical form, using only the characteristic of its nonlinear transformation. It was first applied to the analysis of ISAU with HP and extended by the authors. As a rule, it was used to analyze certain ISAS, including a fuzzy P-controller, and in relation to ISAS with a frequency-dependent fuzzy controller (PI-PID), the studies had a very rough estimate of the dynamic properties of the system. It should also be noted that the approach proposed in the papers is devoid of a methodological nature, which makes it possible to develop engineering tools for the analysis of such ISAS on its basis.

When studying the stability of fuzzy systems, a method based on absolute stability criteria (circular criterion and V.M. Popov criterion) was also used. To use this method, it is necessary to conduct additional studies of the dependence of the nonlinear characteristic to meet a number of requirements. As a rule, it was used to analyze a specific ISAS with fuzzy P-controllers.

Also, work was carried out on the study of fuzzy systems using various approximate methods.

As can be seen, a relatively small number of works are devoted to the study of the stability of ISAS with HP, and, as a rule, all of them are of a private, non-systemic nature. This essentially speaks of the initial stage of development in this direction and suggests a deeper study of the possibilities of each of the listed methods. One of the first attempts at a systematic approach to the study of fuzzy systems belongs to the authors of the work published in 1999. In this work, fuzzy systems are reduced to nonlinear ones, and on this basis, methods are applied to them designed to study the stability of nonlinear systems. As the authors themselves note, the work has several significant drawbacks, the first of which is a rather superficial approach to the analysis of fuzzy systems, because there are no clear, systematic methods of analysis using the presented methods. Also, due attention is not paid to the analysis of the influence of HB parameters on the nonlinear HP transformations. The paper does not present any tools for the synthesis and tuning of fuzzy ISAS, which is very important for their practical application. Recent published works devoted to the analysis of ISAS with HP are mainly based on the above methods.

Study of ISAU by the method of harmonic balance

As was shown in the previous chapter, the intelligent controller performs some non-linear transformation, as a result of which it becomes possible to improve the quality of control in such systems. But at the same time, the presence of nonlinear elements in the ACS circuit, as is known, can lead to various problems associated with the system dynamics. In particular, the regions of stability on the plane of system parameters change (compared to linear systems), and it is necessary to investigate both equilibrium positions and processes. Of great importance is the study of periodic regimes peculiar to nonlinear systems.

For the study of periodic oscillations in ISAS, the method of harmonic balance seems to be promising, which has found wide application in the engineering practice of analysis and synthesis of nonlinear ACS.

This method allows not only to study periodic oscillations in automatic control systems, but also to indirectly evaluate the quality of control of nonlinear systems. The last aspect is extremely important from the point of view of the prospects for solving the ambiguous problem of tuning the fuzzy controller to the required quality of control.

Since intelligent ACS, as has been repeatedly noted, are designed to provide alternative control algorithms for complex dynamic objects operating under the influence of internal and external uncertainty factors, it should be emphasized that these objects, as a rule, have a fairly high dimension and, therefore, to a large extent satisfy the requirements of the filter hypothesis. And hence the accuracy of the results, which will be provided by the harmonic balance method, may be quite acceptable for practical use.

When studying intelligent systems using the harmonic balance method, a methodological problem arises, due to the fact that it was developed for ACS with one non-linear element having one input and one output, and in ISAS with HP there are several such non-linear elements, so it is required to build an HP model, allowing to apply the method of harmonic balance.

In the general case, the block diagram of an intelligent automatic control system with a fuzzy controller (HP) can be represented as a series connection of a fuzzy computer (HC) having h - inputs with linear dynamic links connected to them, and one output, and a control object (OC) with a transfer function Woy(s) (Fig. 2.1), where g(t) is the command signal, (for mechanical systems this is position, speed, acceleration, etc.), u(t) is the control signal, y(t) - output signal of the executive engine, e(t) - control error signal, s - Laplace operator.

A fuzzy controller can be built on the basis of two types of structures: the first type is a fuzzy controller with parallel one-dimensional fuzzy calculators HBI (in Fig. 2.2, for example, a block diagram of a fuzzy PID controller of the first type is shown) and the second type is with a fuzzy calculator with a multidimensional input (Fig. 2.3 shows a block diagram of a fuzzy PID controller of the second type).

Taking into account the nonlinear nature of the transformations in HP, shown in the first chapter, to study periodic oscillations in ISAS, we will use the harmonic balance method.

To apply the harmonic balance method, we will consider a fuzzy controller as a nonlinear frequency-dependent element with one input and one output. The study of self-oscillations in the ISAS, shown in Fig. 2.1, will be carried out at g(t) = 0. Let us assume that a sinusoidal signal e(t) = A sin a t acts at the HP input. The spectral representation of the output signal HP is characterized by terms of the Fourier series with amplitudes U1, U1, U3... and frequencies CO, 2b), bco, etc. Taking into account the fulfillment of the filter hypothesis for the ISAS control object, we will assume that in the spectral decomposition of the signal y(f), at the output of the control object, the amplitudes of higher harmonics are significantly less than the amplitude of the first harmonic. This allows, when describing the signal y(t), to neglect all higher harmonics (due to their smallness) and to assume that y(t) s Ysm(cot + f).

Investigation of the absolute stability of ISAU with HP

In the previous chapter, the harmonic balance method was considered for solving problems of analysis and synthesis of intelligent in small automatic control systems with sequential controllers. Despite the known limitations of this method, the results of studying self-oscillations on the plane of control system parameters in many cases give an exhaustive result at the analysis stage and quite constructive approaches to the synthesis of controller parameters for a given oscillation index.

At the same time, it is known that for many nonlinear control systems, the study of only periodic motions is incomplete and does not adequately reflect the dynamic processes in the system. Therefore, undoubtedly, it is of interest to develop methods that allow us to study the absolute stability of both the equilibrium position and the processes in intelligent control systems.

Considering the features of nonlinear transformations carried out in intelligent controllers discussed in Chapter I, it can be assumed that today the development of methods for studying absolute stability seems to be the most realistic for ISAS with fuzzy controllers of the first type, since such systems can be reduced to multi-loop nonlinear systems, methods studies of which are described in the literature.

Since ISAS with HP of the first type is generally a nonlinear multi-loop system, it is advisable to first consider the well-known criteria for the absolute stability of the equilibrium position and processes for such nonlinear systems.

A generalized block diagram of a multiloop nonlinear ACS is shown in fig. 3.1, where % and a are scalar vectors.

Denote by u(V the class of non-linear blocks (3.3) with the following properties: for h \ the inputs are o-jit) and the outputs %.(t) of the non-linear blocks are related (for ov (/) 0) by the relations: %) "" and=1 m (3-9) where cCj,fij are some numbers. In addition, the matrix inequality \j3 (t)(t)) 0 must be satisfied. (3.10) The circular criterion for the absolute stability of processes for systems with several nonlinearities (Fig. 3.1.) has the following formulation:

Let the equations of the linear part of the system have the form (3.1) a, the equations of the nonlinear blocks (3.3). Let all poles of the elements of the matrix Wm(s) be located in the left half-plane (stable linear parts in all contours), a = diag(al,...,ah), f$ = diag(pl,...,J3h) - diagonal matrices with specified diagonal entries. Suppose that for some hxh diagonal matrix d with positive diagonal elements, the frequency condition te B(N »_N Fig.3.2.b.

In this case, it should be taken into account that the linear part of the system will also change. Thus, taking into account the above features of the criterion for the absolute stability of the equilibrium position for multidimensional nonlinear systems, we formulate it for the ISAS with HP.

As already noted in the first chapter, HB performs a non-linear transformation. It should be noted that the nonlinear characteristics %(&), implemented by fuzzy calculators, have limitations in amplitude, therefore, at Yj - the lower boundary of the sector can be equated to zero a = O, hence it follows (р (а) o ? -±L = juJ pj, j = \,...,h

If in the process of setting up a fuzzy controller of the first type it turned out that one of the fuzzy calculators implements nonlinear transformations (Pji j) (Fig. 3.3a) that do not satisfy the conditions of the class G\, then it is necessary to carry out structural transformations in accordance with remark 3.4. Naturally, in order to preserve the condition of equivalence of the original and transformed structures, it is necessary to make appropriate changes to the linear part.

If there is a neutral linear part in one of the ISAS circuits (Fig. 3.4), in order to apply the criterion of absolute stability of the equilibrium position (3.7), it is necessary to cover the negative feedbackє 0, both the corresponding linear part and HBj with the non-linear characteristic Pj(crj). For ->0, criterion (3.7) will be applicable for all frequencies except for ω = 0. Considering what has been said, the criterion for the absolute stability of the equilibrium position for ISAS with HP of the first type can be written in the following form.

Let the equations of the linear part of the ISAE have the form (3.1), the nonlinear characteristics of the NV of the fuzzy controller correspond to (3.3), where the functions (PjiGj) satisfy the conditions of the class G . Let all the poles of the elements of the matrix Wm (s) be located in the left half-plane or have one pole on the imaginary axis (stable or neutral linear parts in all contours). Let us introduce a diagonal matrix /Jj = diag(jti[ ,..., juh) with diagonal entries ju ,...,juh , where Mj = if Mj =, and diagonal matrices rd = diag(Tx,..., rh), 3d =diag(3l,...,3h), where all Td 0. Suppose that for some m 0, 3= and all - oo co + oo, except for oo = 0, the relations

Using Genetic Algorithms to Solve Problems of Automation of Synthesis and Tuning of Fuzzy Controllers

The implementation of the procedure for automated synthesis of HP parameters based on GA necessitates the solution of three main tasks: 1) determining the functional features of the GA operation; 2) determining the method of encoding HP parameters in the chromosome; 3) implementation of the objective function.

Standard genetic algorithms, by definition, operate with a set of elements, which are called chromosomes in this work, they are bit strings with an encoded description of potential solutions to the applied problem. In accordance with the generalized block diagram for constructing a genetic algorithm (Fig. 4.1), within the framework of its next cycle, each of the chromosomes of the existing set is subjected to some assessment, based on an a priori given criterion of "utility". The results obtained make it possible to select the "best" specimens for generating a new population of chromosomes. In this case, the reproduction of descendants is carried out due to a random change and cross-crossing of the corresponding bit strings of the parent individuals. The evolution process is stopped when a satisfactory solution is found (at the stage of assessing the usefulness of chromosomes), or after the allotted time has elapsed.

It should be noted that the inheritance of the characteristics of the elite representatives of the previous population in the next generation of individuals provides an in-depth study of the most promising parts of the solution search space. At the same time, the presence of mechanisms for random mutation of bit strings of selected elements guarantees a change in search directions, preventing falling into a local extremum. Such imitation of evolutionary processes makes it possible to ensure the convergence of the search procedure to the optimal solution, however, its effectiveness is largely determined by the parameters of the genetic algorithm and the set of initial data specified taking into account the specifics of the applied problem. These include the type and dimension of the chromosome, the size of the population, the function of evaluating the usefulness of chromosomes and the type of selection operator, the criterion for stopping the search procedure, the probability of performing a mutation, the type of crossing operation, etc. HP Parameter Coding

Despite the seeming simplicity of constructing and implementing genetic algorithms, their practical use is also associated with the complexity of choosing a method for coding the search space for solutions to a specific applied problem in the form of a chromosome with the further formation of an objective function, by calculating the value of which an assessment and subsequent selection of individuals in the current generation will be carried out for automatic generation of the next one.

So, when synthesizing fuzzy controllers in accordance with the Mamdani scheme, the set of tuning parameters that allow obtaining the required quality of control includes the number and relationships of terms of input and output linguistic variables (LP), as well as the form of membership functions (PP) and their placement within the working range.

In any case, the structure and dimension of the chromosome encoding the HP parameters should be determined taking into account a number of specific factors, including those characterizing the chosen way of representing membership functions.

Stepanov, Andrei Mikhailovich