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Generalized models of a production system of fuzzy conclusion

Davron Ziyadullaev“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Tashkent, 100000, UzbekistanDilnoz Muhamediyeva“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Tashkent, 100000, UzbekistanZafar Abdullaev“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Tashkent, 100000, UzbekistanSharofiddin Aynaqulov“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Tashkent, 100000, UzbekistanKhasanturdi KayumovTashkent State Transport University, Tashkent, Uzbekistan
E3S Web of Conferencesjournal2023en
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The aim of the research is to study the models, rules, and fuzzy inference engines, which occupy the main place in the knowledgebase, and models of the logic inference engines and simulation modeling, focused on supporting the adoption of semi-structured decisions under uncertainty. This implies the relevance of the task of developing theoretical and methodological tools that provide automation of the processes of fuzzy inference systems. Research methods are the theory of fuzzy sets and fuzzy logic. New scientific results are the design and formation of a set of production rules from a given set of admissible ones, with specific values of conditions and conclusions for describing three types of fuzzy models of the processes and tasks under study. Using modules of standard algorithms and programs, algorithms and a program for solving problems of fuzzy inference systems and making semi-structured decisions based on the constructed fuzzy logic model were developed. This problem is solved by formalization methods based on the theory of algorithmization, fuzzy sets, and fuzzy inference.

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