COMPLEX AI SYSTEM BASED ON MIVAR AND NEURAL NETWORKS FOR DETECTION OF PRODUCT DEFECTS IN BAKERY PRODUCTS PRODUCTION

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Golobokov Alexander Mikhailovich
student
Institute of Artificial Intelligence of RTU MIREA, Moscow, Russia.
Research interests: artificial intelligence, mivar technologies of logical artificial intelligence, smart production systems, information processing, pattern recognition, cybernetics.

Kohanov Artem Aleksandrovich
student
Institute of Artificial Intelligence of RTU MIREA, Moscow, Russia.
Research interests: artificial intelligence, mivar technologies of logical artificial intelligence, smart production systems, information processing, pattern recognition, cybernetics.

Katz Ilya Dmitrievich
student
Institute of Artificial Intelligence of RTU MIREA, Moscow, Russia.
Research interests: artificial intelligence, mivar technologies of logical artificial intelligence, smart production systems, information processing, pattern recognition, cybernetics.

Mutina Elena Igrevna
Candidate of Technical Sciences
Senior Researcher at JSC Research Institute of Computing Complexes.
Research interests: computing complexes, artificial intelligence, expert systems, logic, mivar technologies of logical artificial intelligence, information processing, decision-making, pattern recognition, natural language understanding, cybernetics

Adamova Larisa Evgenievna
Candidate of Psychological Sciences, Associate Professor, Associate Professor of the Department of General Psychology and Labor Psychology.
University (RosNOU).
Research interests: psychology, artificial intelligence, mivar technologies of logical artificial intelligence, information processing.

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UDC 004.891+ 7.52 + 4.896 + 681.518 + 65.011.56
DOI 10.24412/2413-7383-27-42
Language: Russian
Annotation: The production of bakery products includes many stages, from the preparation of raw materials to the packaging of finished products. One of the key tasks in this area is to ensure product quality control. Possible violations in the production environment of bakery products are analyzed, a system of factors influencing them is established, and a corresponding forecasting model is built. It is substantiated that in the field of smart production systems for the production of bakery products, it is possible and advisable to create a mivar expert system to increase the intellectualization of decisionmaking and information processing. A mivar knowledge base has been developed, including 54 of new opportunities for further implementation of the mivar approach in combination with machine learning algorithms and neural networks at various stages of production is substantiated. This will mivar «If, Then rules», to detect product defects in the production of bakery products. The potential improve the quality of manufactured products and move to a new level of creating automated control systems for production systems in the field of bakery production.
Keywords:mivar approach, artificial intelligence, defect detection, Wi!Mi RAZUMATOR, mivar technologies, mivar expert systems, machine learning, bakery products, machine vision.

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Release: 1(36)'2025
Chapter: SYSTEM ANALYSIS, INFORMATION MANAGEMENT AND PROCESSING, STATISTICS
How to quote: Golobokov A.M., Kokhanov A.A., Katz I.D., Mutina E.I., Adamova L.E.. COMPLEX AI SYSTEM BASED ON MIVAR AND NEURAL NETWORKS FOR DETECTION OF PRODUCT DEFECTS IN BAKERY PRODUCTS PRODUCTION // Problems of artificial intelligence. 2025. №1.

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