Parameter estimation of fuzzy controller using genetic optimization and neurofuzzy networks

Authors:
Sungkwun Oh;Seokbeom Roh;Taechon Ahn
Affiliations:
Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
A comparative design and tuning for conventional fuzzy control

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

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Abstract

In this study, we introduce a noble neurogenetic approach to the design of fuzzy controller. The design procedure dwells on the use of Computational Intelligence (CI), namely genetic algorithms and neurofuzzy networks (NFN). The crux of the design methodology is based on the selection and determination of optimal values of the scaling factors of the fuzzy controllers, which are essential to the entire optimization process. First, the tuning of the scaling factors of the fuzzy controller is carried out, and then the development of a nonlinear mapping for the scaling factors is realized by using GA based NFN. The developed approach is applied to a nonlinear system such as an inverted pendulum where we show the results of comprehensive numerical studies and carry out a detailed comparative analysis.