The design of fuzzy controller by means of evolutionary computing and neurofuzzy networks

Authors:
Sung-Kwun Oh;Seok-Beom Roh
Affiliations:
Department of Electrical Engineering, The University of Suwon, Gyeonggi-do, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - 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 propose a new design methodology to design fuzzy controllers. This design methodology results from 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.