APPLICATION OF THE FUZZY SETS THEORY IN THE MANAGEMENT OF TECHNOLOGICAL PROCESSES FOR THE PRODUCTION OF AUTOMATION TECHNICAL MEANS
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https://doi.org/10.5281/zenodo.16521016Keywords:
modeling, technological object, fuzzy environment, operational identification, problem dimensionAbstract
It is established that the main problems of algorithmization for the management of technological processes under conditions of uncertainty are the high dimensionality of the task, the need for prompt identification of technological objects of control and the choice of optimal control influences. It is shown that these problems can be solved using fuzzy logic and decision theory.
A method for formalizing fuzzy concepts based on an objective probabilistic approach is proposed, which increases the reliability of modeling results. Based on fuzzy logic, a method for identifying multidimensional technological objects has been developed, in which, in order to reduce the dimensionality of the problem, a conjunctive inference rule is used instead of the generally accepted disjunctive one to build a relationship between a multidimensional input and output, which simplifies the inference procedure and leads to a more compact and convenient model for practical application.
References
Zheldak T., Koryashkina L., Us S. Fuzzy sets in management and decision-making systems. Dnipro: NTU "DP". 2020. 387 p.
Lievi L. I. Intelligent information technology in the identification and management of complex technical objects in conditions of uncertainty: a monograph. Poltava: Yurii Kondratiuk National University. 2021. 194 р.
Kasianiuk V. S., Maliutenko L. M., Polshcha M. Modeling fuzzy sets by means of possibility theory. Scientific notes of NaUKMA. Т. 138. 2012. pp. 30-34.
Ladaniuk A. P., Smitiukh Ya. V., Vlasenko L. O. System analysis of complex control systems. Kyiv: National University of Food Technologies. 2013. 274 p.
Nguyen A.-T., Taniguchi T., Eciolaza L., Campos V., Palhares R. and Sugeno M. Fuzzy Control Systems: Past, Present and Future. IEEE Computational Intelligence Magazine. Vol. 14. No. 1. pp. 56-68.
Siddique N. Fuzzy control. In: Intelligent Control. Studies in Computational Intelligence. Vol. 517. Springer, Cham. 2014. pp. 95-135.
Khodashynskyi I. A. Identification of fuzzy systems: methods and algorithms. Management problems. 2009. Vol. 4. pp. 15-23.
Hunko I. V., Halushchak O. O., Kravets S. M. Analysis of technological systems. Justification of engineering solutions. Vinnitsa: VNAU. 2019. 216 p.
Yehorshyn O. O., Maliarets L. M., Sinkevych B. V. Handbook of mathematical statistics with examples of calculations in MatLab: a practical textbook. PART 2. Kharkiv: KhNEU Publishing House. 2009. 508 p.
Buketov A. V. Identification and modeling of technological objects and systems. Ternopil: SMP "Taip". 2009. 260 p.
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