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• W3201454062 abstract "The paper proposes a method for optimizing the rule base of an intelligent controller based on fuzzy clustering for controlling an object operating in changing conditions. For modeling, a hybrid model of a technical object control with the action of a disturbing influence at the start is used. The model is implemented based on a PID-classical and intelligent PD-FUZZY-controller (IPD-FUZZY-controller) with the generated structure of a Sugeno-type fuzzy inference system and a developed model of an adaptive neuro-fuzzy inference system. In the developed hybrid model, the rule base for the IPD-FUZZY-controller has formed automatically based on data obtained from the PID-classical controller with subsequent training and fuzzy clustering, which allows reducing the number of membership functions and the number of fuzzy inference rules during the synthesis of the IPD-FUZZY-controller and ensuring effective and robust control of the object functioning in unforeseen external situations. A hybrid model proved the effectiveness of the proposed method before and after fuzzy clustering." @default.
• W3201454062 created "2021-09-27" @default.
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• W3201454062 date "2021-01-01" @default.
• W3201454062 modified "2023-10-16" @default.
• W3201454062 title "Optimization of the Intelligent Controller Rule Base Based on Fuzzy Clustering for Controlling an Object Operating in Changing Conditions" @default.
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• W3201454062 doi "https://doi.org/10.1007/978-3-030-87034-8_12" @default.
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