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The main objective of a hybrid expert system is to improve production processes, minimize downtime and improve product quality. The system’s hybrid design allows it to handle different scenarios, adapt to new situations and continuously improve its efficiency. However there are problems of these systems’ adaptation to features of various production types, and of high cost and complexity of their development and implementation. In this work, a model of a hybrid expert system is presented combining rule-based reasoning and machine learning methods and designed for industrial production. The system is represented as a cyclic graph reflecting its ability to continuously learn and adapt. The proposed model of hybrid expert system is a reliable framework for developing an expert system for modern industrial production.
Alexey V. Gorodilov
National Research University of Electronic Technology (Russia, 124498, Moscow, Zelenograd, Shokin sq., 1)
Andrey V. Chirkov
National Research University of Electronic Technology (Russia, 124498, Moscow, Zelenograd, Shokin sq., 1)

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