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A software development system for fuzzy control

Published online by Cambridge University Press:  09 April 2001

Jie Yang ,
Yingkai Guo  and
Xin Huang
Jie Yang
Affiliation:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao-Tong University, Shanghai 200030 (P.R. of China)
Yingkai Guo
Affiliation:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao-Tong University, Shanghai 200030 (P.R. of China)
Xin Huang
Affiliation:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao-Tong University, Shanghai 200030 (P.R. of China)
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Abstract

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Fuzzy control has been widely applied in industrial controls and domestic electrical equipment. The automatic learning of fuzzy rules is a key technique in fuzzy control. In this paper, a software development system for fuzzy control is presented. Since the learning of fuzzy rules can be seen as finding the best classifications of fuzzy memberships of input-output variables in a fuzzy controller, it can also be seen as the combination optimization of input-output fuzzy memberships. Multi-layer feedforward network and genetic algorithms (GA) can be used for the automatic learning of fuzzy rules. The algorithms and their characteristics are described. The software development system has been successfully used for the design of some fuzzy controllers.

Keywords

Fuzzy controlRule generationMulti-layer feedforward networkGenetic algorithm
Type
Research Article
Information
Robotica , Volume 18 , Issue 4 , July 2000 , pp. 375 - 380
Copyright
© 2000 Cambridge University Press