NDLI: Learning and tuning fuzzy logic controllers through genetic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Shuqing Zeng Yongbao He
Copyright Year	1994
Description	Author affiliation: Dept. of Comput. Sci., Fudan Univ., Shanghai, China (Shuqing Zeng; Yongbao He)
Abstract	This paper reviews the current fuzzy control technology from the engineering point of view, and presents a new method for learning and tuning a fuzzy controller based on genetic algorithm (GA) for a dynamic system. In particular, it enhances the fuzzy controller with self-learning capability for achieving the prescribed control objective into near optimal manner. The methodology first adopts expert experiences, it then uses the GA to find the fuzzy controller's optimal set of parameters. In using GA, we must define an objective function to measure the performance of the controller. Since the behaviour of the dynamic system is hard to predict, a three-layer forward network has been adopted. For the purpose to accelerate the learning process, a conventional simplex optimal algorithm is used to reduce the search space. Finally, an example is given to show the potential of the method.
Starting Page	1632
Ending Page	1637
File Size	365498
Page Count	6
File Format	PDF
ISBN	078031901X
DOI	10.1109/ICNN.1994.374400
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Genetic algorithms Fuzzy control Fuzzy sets Optimal control Humans Genetic mutations Fuzzy set theory Control systems Neural networks
Content Type	Text
Resource Type	Article

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