Fuzzy Production Model Research Articles

Нечеткая динамическая онтологическая модель для поддержки принятия решений по управлению энергоемкими системами на основе прецедентов

The article discusses the features of applying the precedent approach when managing complex energy-intensive systems in the context of the need to take into account various energy, technical, environmental and operational indicators, as well as the uncertainty of many internal and external factors influence. This leads to the presence of a large amount of semi-structured information that can be presented using various scales, which determines the prospects of using the precedent approach. The proposed fuzzy ontological model for supporting decision support based on precedents is described, characterized by the use of dynamic concepts, as well as concepts in the form of different scale numerical and linguistic variables. An algorithm for assessing the proximity of precedents based on an ontological model is proposed, which differs by taking into account the dynamic aspects of changes in the state of controlled systems. The developed algorithms for fuzzy inference for decision support based on precedents are presented, which allow the use of both linguistic and numerical variables as input characteristics of the fuzzy production model, as well as using various logical connections between the rules pre-requisites. The software that implements the developed model and algorithms is described. Particular attention is paid to the modified fuzzy inference component, implemented using Python 3.8.7 language tools. To implement the user interface of the specified component, the cross-platform graphic library Tkinter was used. The results of computational experiments using real data obtained during the operation of an energy-intensive system for processing fine ore raw materials, including a conveyor-type roasting machine, are presented. Minimization of specific total costs for thermal and electrical energy was considered as a criterion for the effectiveness of management decisions. The outcome obtained showed that the proposed model and software make it possible to obtain a result comparable to the one of using complex analytical dependencies, while ensuring a reduction in time and financial costs.

Особенности предоставления финансовой поддержки субъектам малого бизнеса рыбной отрасли с использованием продукционной модели базы знаний

The article highlights the procedure of selecting applications for state financial support for small businesses in the fishing industry. Criteria for expert evaluation of applications have been defined taking into account industry specifics and including the following blocks: “Compliance with formal requirements”, “Type of activity”, “Justification of the requested funding”, “Socio-economic contribution to the region”. The principles of developing the evaluation criteria are generalized. The examination procedure algorithm taking into account the observance of formal requirements at the first stage of the expertise has been worked out. The algorithm provides for the functionality of clarifying the type of activity and conducting assessment according to the relevant criteria. Aggregated indicators of expert assessments reduced to a linguistic description “high”, “medium”, “low” are obtained. The system of trapezoidal membership functions on the 01-carrier (five-factor 01-classifier) is illustrated. The applicability of fuzzy logic tools for the tasks of multi-criteria assessment and management decision-making is substantiated. A base of rules for fuzzy production has been developed, consisting of 9 rules and a set of management decisions on financial support. The implementation of the decision-making procedure as a fuzzy production model built on using FuzzyLogicToolbox is shown. The conclusion about the possibility of using the results in the design of intelligent decision support systems in the field of financing small and medium-sized businesses in the fishing industry is made. It has been stated that the fuzzy production system and the expert evaluation algorithm can be projected onto other branches of the national economy as a result of the configuration of the constituent elements.

FUZZY REGULATOR OF THE ROD SUPPLY SYSTEM OF ARDUINO-BASED CROSS-WEDGE ROLLING MILL IN LABVIEW ENVIRONMENT

The main purpose of scientific work is to develop a regulator based on fuzzy logic for an electronic control system for the supply of bar billets in automated cross-wedge rolling complexes.
 Cross-wedge rolling belongs to the advanced energy-saving technologies where technological processes based on metal cutting are replaced by economic processes of plastic forming of details. The analysis showed that coefficient increase of metal utilization requires loss decrease arising from the supply inaccuracy of the bar workpiece to the length of the part. The task is to reproduce the given law of motion of the actuator, i.e. to ensure maximum speed, smooth acceleration and braking with the possibility of a shockless stop at the end of the stroke. The solution of this problem is proposed to be carried out by entering the fuzzy control unit into the structure of the system for controlling the supply..
 The hardware basis of the control system in this work is the Arduino hardware and software complex based on the Atmel AVR microcontroller. The software model of the control system is developed in the graphical programming environment LabVIEW, which is widely used as a standard tool for data collection and control of devices in industry, education and research laboratories. The construction of a fuzzy regulator and its implementation are considered using Fuzzy Logic Designer.
 On the basis of systematized knowledge about the object of research, its characteristics and features, linguistic input and output variables were selected, as well as membership functions and a fuzzy production model of knowledge was formulated. The configuration of the control system depends on the number of controlled outputs and controlled input data and in this case the MISO information structure is applied. The paper presents the front panel of the virtual device developed by LabVIEW, which is designed to display information about the current state of the system. The purpose of the developed fuzzy regulator model is to output the value of the initial air pressure in the cavity of the actuating pneumatic cylinder, which will create an air cushion in the process of advancing the piston, which has properties similar to those of a normal spring. Smooth stopping of the piston is provided by the coordinated choice of rigidity of a pneumatic spring taking into account length of a detail and weight of a rod and allows us to reduce kinetic energy in an end point of positioning that increases accuracy of supply.
 The analysis of research results confirmed the efficiency and adequacy of the model used and the developed set of operation rules of the fuzzy regulator. Testing of the control system on parts of different lengths confirmed a reduction in total losses and an increase in the utilization of the metal.

Optimum planning of experimental research at the biogas plant

Biogas energy, being one of the important components of ecological agricultural production, on the one hand, allows you to conduct successful disposal of crop and livestock waste, receiving organic fertilizers, and on the other hand makes it possible to provide heating and electricity to greenhouses, farms and other industrial buildings and buildings. At the same time, bioenergy technologies require improvement and development, taking into account the specifics of the agro-industrial complex in which they are used. The problems of organizing scientific research at a biogas station are considered. Based on the analysis of the functioning of the biogas plant, a mathematical formulation of the problem of constructing a schedule for conducting experiments taking into account the costs of equipment retooling is formulated. To determine the coefficients of the objective function of the constructed optimization problem, it is proposed to use expert technologies and a fuzzy inference procedure based on a fuzzy production model of knowledge about the subject area. To construct a solution to the optimization problem, along with the classical methods of discrete linear optimization, it is proposed to use evolutionary genetic algorithms that are effective in solving problems of large dimension.

ТЕХНОЛОГІЯ ВПРОВАДЖЕННЯ СЕРЕДОВИЩА MATLAB В ДОСЛІДЖЕНІ МОДЕЛІ ЗАГРОЗ ІНФОРМАЦІЙНОЇ БЕЗПЕКИ

The paper presents practical research in the field of modeling information security threats. There are several ways to determine the assessment of the level of information security in the context of threats that could potentially affect their information resources. The general scheme of information system and fuzzy production model of information security is developed. Using this approach, a study of the modeling of threats to IT goals.

INTELLIGENT APPROACH FOR DIAGNOSIS ELECTRICAL EQUIPMENT OF INDUSTRIAL FACILITIES

This paper is devoted to actual problem of ensuring of trouble-free functioning and a high level of fault tolerance of industrial facilities’ electrical equipment (EE) by diagnosing them during their operation. However, when diagnosing equipment, a number of challenges arise related to the following problems: a large number of monitored parameters, the presence of not only statistical information but the values of linguistic variables that are used by staff on duty, insufficient and incomplete input information. Proposed approach to diagnosing EE based on a composition of fuzzy cognitive model with a fuzzy vertices, links and fuzzy-functional cognitive model with functional links for generating intermediate information, and as well as fuzzy-production model of an upper level for taking final diagnostics decisions. The proposed approach is based on the functional model of EE diagnosing in IDEF0 notation which reflects the sequence of all necessary functions related to the diagnosing and provides the identification of the key data flows and a more comprehensive understanding of the process. The paper also provides a formulation of the fuzzy-set method that is based on the construction of functional dependencies formed in clear mapped set “the occurrence frequency of for equipment's dangerous situation”. This method’s novelty lies in the new formulation of the problem in accordance with the latest scientific achievements and its solution that was not previously considered in the well known sources of literature. The suggested method is allows to calculating the predicted values of controlled parameters of EE in case of statistic and fuzzy source data. The proposed approach to the EE diagnosis at industrial objects makes it possible to improve the accurate and complete EE diagnosis through the knowledge of the staff on duty, thereby resulting in timely repair and maintenance.

Нечеткая продукционная модель знаний для корректировки результатов математического моделирования и оптимизации гальванического процесса при его практической реализации

The main parameters (technological conditions and geometric factors) the optimization of which is used to simulate electroplating processes with specified characteristics (coating thickness nonuniformity over the part surface and process duration) are considered. The mathematical statement of the electroplating process optimization problem written in the form of additive convolution of partial criteria is presented. The composition of the process mathematical model’s system of equations and the algorithms for finding its solution are described. It is pointed out that the parameters adopted for calculations according to the mathematical model may differ considerably from the actual parameters. To eliminate the need of repeatedly solving the optimization problem, it is proposed to use the experience, knowledge and intuition of a specialist in electroplating technology who directly conducts the technological process. The proposed approach can be implemented using a fuzzy production model of knowledge that takes into account technological conditions and geometric factors in an integrated manner. The aim of the developed model is to obtain close-to-optimal experimental values of partial criteria taking into account the deviations of the found parameter values from their actual values by adjusting them. The input and output variables with terms and membership functions are defined. The key electrochemical regularities required for constructing the production model knowledge base, which contains a system of rules based on conditional statements in the form of "IF ... THEN ..." are formulated. The application of the developed model is considered taking as an example the problem of correcting the results of modeling and optimizing the nickel plating process in a sulfate electrolyte for parts with complex surface shapes. For the selected forms of parts, the optimization problem was solved, the results of which were implemented in the plant without and with correction according to the developed knowledge model. The obtained results were compared by calculating the relative deviation of the predicted value of the criterion from its experimental value. It is shown that the correction of the found optimal parameters has an effect on the target criterion with increasing the weight of the first term (the coating thickness nonuniformity) due to its being calculated for an object with distributed coordinates and, as a consequence, due to a nonlinear dependence.

Using fuzzy rule-based knowledge model for optimum plating conditions search

The paper discusses existing approaches to plating process modeling in order to decrease the distribution thickness of plating surface cover. However, these approaches do not take into account the experience, knowledge, and intuition of the decision-makers when searching the optimal conditions of electroplating technological process. The original approach to optimal conditions search for applying the electroplating coatings, which uses the rule-based model of knowledge and allows one to reduce the uneven product thickness distribution, is proposed. The block diagrams of a conventional control system of a galvanic process as well as the system based on the production model of knowledge are considered. It is shown that the fuzzy production model of knowledge in the control system makes it possible to obtain galvanic coatings of a given thickness unevenness with a high degree of adequacy to the experimental data. The described experimental results confirm the theoretical conclusions.

ОЦЕНКА КАЧЕСТВА СИСТЕМ СОТОВОЙ СВЯЗИ НА ОСНОВЕ НЕЧЕТКОГО ВЫВОДА

Quality of mobile system communication is significantly defined by completeness and reliability of information on a status of objects of radio-electronic means. In most cases, the probability of decline in quality increases with untimely response of field services to the existing back couplings of the control system and the analysis of parameters of quality. Modern objects of radio-electronic means are known to be difficult systems having technical intelligence. Therefore, when processing the arriving information on statuses from such objects there happen such situations when use of traditional methods brings to a row of difficulties in their analysis. It occurs for the reasons of insufficiency of prior information, multiparametricity, volume of computing character, a non-standard of situations, a subjective factor of operators. In this regard for processing of such information it is offered to use the theory of fuzzy sets. Parameters of monitoring in the system of mobile communication have been determined. Using the field power level as a key parameter is justified because the whole range of the related secondary parameters of communication quality on input of a receiving device depends on signal power of a communication system in a point of reception. The fuzzy production model has been developed. Each parameter is provided to a linguistic variable for which there is defined a term - set of values. The fuzzy production rules of the model are realized using Fuzzy Logic Toolbox of the software MATLAB on the basis of Mamdani's algorithm.

An adaptive neuro-fuzzy inference system for assessment of risks to an organization’s information security

Sergey A. Glushenko - Senior Lecturer, Department of Information Systems and Applied Computer Science, Rostov State University of EconomicsAddress: 69, Bolshaya Sadovaya Street, Rostov-on-Don, 344002, Russian FederationE-mail: gs-gears@yandex.ru This article explains the importance of applying risk assessment in the implementation of information security systems. It is considered the most common risk assessment procedure and entails application of fuzzy logic theory for this purpose. The paper describes the proposed fuzzy production model (FPM), which defines seven input linguistic variables describing risk factors, four output linguistic variables that characterize different areas of information security risks, as well as four base rules. It is noted that the FPM is the first approach to the subject area and requires optimization to minimize the model’s output errors. The most common methods of optimization of fuzzy models parameters are examined, and the advantages of applying methods based on neuro-fuzzy networks (NFN) are justified. The article describes the process of converting fuzzy model elements, such as unit fuzzification, rule base unit and unit defuzzification, into fragments of the neural network. The result of this process is a neuro-fuzzy network corresponding to the fuzzy model. Formation of the developed NFN is based on an adaptive neuro-fuzzy inference system (ANFIS), using the specialized Neuro-Fuzzy Designer package of MATLAB software. The model was trained by a hybrid method which represents a combination of the methods of least squares and backpropagation. The result of this process is optimization (setting) the parameters of membership functions of input linguistic variables. Application of neuro-fuzzy modeling made it possible to obtain a more appropriate fuzzy production model which is able to conduct linguistic analysis of the risks of an organization’s information security. The information obtained with its help allows IT managers to determine risk priorities and to develop effective action plans to reduce the impact of the most dangerous threats.[1] This research has been carried out with financial support of RFBR within the framework of scientific project No. 16-31-00285 “Fuzzy logic methods and models in risk management decision support systems”

A fuzzy production model with probabilistic resalable returns

In this paper, a fuzzy production inventory model with resal- able returns has been analysed in an imprecise and uncertain environment by considering the cost and revenue parameters as trapezoidal fuzzy num- bers. The main objective is to determine the optimal fuzzy production lotsize which maximizes the expected profit where the products leftout at the end of the period are salvaged and demands which are not met directly are lost. The modified graded mean integration representation method is used for defuzzification of fuzzy parameters of production lotsize and expected profit. An example is presented to illustrate the method applied in the model.

A New Fuzzy Reasoning Model for Decision Support System

This paper proposes a new fuzzy reasoning model, involved the knowledge representation sub–model based on synthetic language system, the operators and the inference rules it uses, and, the meta–rules for the firing rule selection. The model is used for the knowledge – based decision support to make DSS enable to support human decision making process in which information coming from real – world is imperfect. The new model is a combination of fuzzy production model based on Zadeh's fuzzy logic and traditional production model based on traditional two–valued or multi–valued logic. The new model is efficient in inference with fuzzy information by means of the combining the inference rules of fuzzy logic with the variety of the matching rules of the traditional production systems and the quantifier – tables, make it possible for explaining the inference process and inference consequences, that is, the decision support process.