The design of fuzzy controller by means of genetic algorithms and NFN-based estimation technique

Authors:
Sung-Kwun Oh;Jeoung-Nae Choi;Seong-Whan Jang
Affiliations:
Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea;Department of Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea;Department of Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 2
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Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Stable adaptive fuzzy controllers with application to inverted pendulum tracking

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

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Abstract

In this study, we introduce a neurogenetic approach to the design of fuzzy controllers. The design procedure exploits the technology of Computational Intelligence (CI) focusing on the use of genetic algorithms and neurofuzzy networks (NFN). The crux of the design concerns 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.