A software development system for fuzzy control

Authors:
Jie Yang;Yingkai Guo;Xin Huang
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao-Tong University, Shanghai 200030 (P.R. of China);Institute of Image Processing and Pattern Recognition, Shanghai Jiao-Tong University, Shanghai 200030 (P.R. of China);Institute of Image Processing and Pattern Recognition, Shanghai Jiao-Tong University, Shanghai 200030 (P.R. of China)
Venue:
Robotica
Year:
2000

Citing 3
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Robotica

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Abstract

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.