NDLI: Parameter optimization for a class of general TS fuzzy controllers via a new DNA-based genetic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Lihong Ren Yongsheng Ding
Copyright Year	2004
Description	Author affiliation: Coll. of Inf. Sci. & Technol., Donghua Univ., Shanghai, China (Lihong Ren; Yongsheng Ding)
Abstract	We propose a new approach to DNA-based genetic algorithms (DNA-GA) to optimize the design parameters for a class of general Takagi-Sugeno fuzzy controllers. The optimized design parameters are the input fuzzy sets and the linear consequent of the rules. The DNA-GA uses virus DNA encoding method stemmed from the structure of the biological DNA. Specifically, we use the frameshift decoding method of the virus DNA to encode the design parameters of the fuzzy controllers. The genetic operators of the method are based on the DNA genetic operations. Another novel aspect of our method is the introduction of an adaptive mutation rate, which is tuned by using a fuzzy logic technique. Our encoding method can significantly shorten the code length of DNA chromosomes and is suitable for complex knowledge representation. In addition, the method allows for easy implementation of the genetic operations at the gene level in the DNA-GA. As a demonstration, we show how to implement the new method to optimize the design parameters of the TS fuzzy controller. Computer simulation indicates that the designed fuzzy controller is satisfactory in control of a nonlinear system.
Sponsorship	Zhejian Univ. Nat. Lab. of Ind. Control Technol. IEEE Robotics and Automation Soc. Nat. Natural Sci. Found. of China Chinese Assoc. of Automation
Starting Page	2149
Ending Page	2153
File Size	395035
Page Count	5
File Format	PDF
ISBN	0780382730
DOI	10.1109/WCICA.2004.1341966
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy control Genetic algorithms DNA Design optimization Encoding Nonlinear control systems Algorithm design and analysis Takagi-Sugeno model Fuzzy sets Biological information theory
Content Type	Text
Resource Type	Article

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