DocumentCode
596641
Title
Research on optimizing fuzzy controllers based on genetic algorithm
Author
Kaijun Xu ; Chunyan Zhang ; Shuwang Wang ; Hannian Zhang
Author_Institution
Sch. of Electr. & Inf., Nanjing Coll. of Inf. Technol., Nanjing, China
fYear
2012
fDate
18-20 Oct. 2012
Firstpage
545
Lastpage
548
Abstract
A novel method based on the concepts of genetic algorithm (GA) is proposed to design a fuzzy controller directly from some gathered input-output data. The proposed method can pick up fuzzy rule models and determine the parameters of membership functions of each input variable automatically from adequate datum. And it can optimize parameters of membership functions using a real coded genetic algorithms. Finally, a typical nonlinear function is utilized to illustrate the effectiveness of the proposed method.
Keywords
control system synthesis; fuzzy control; genetic algorithms; nonlinear functions; adequate datum; fuzzy controller design; fuzzy controller optimization; fuzzy rule models; input variable; input-output data; membership function parameter; nonlinear function; real coded genetic algorithm; Accuracy; Approximation algorithms; Approximation methods; Biological cells; Fuzzy systems; Genetic algorithms; Input variables;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4673-1743-6
Type
conf
DOI
10.1109/ICACI.2012.6463223
Filename
6463223
Link To Document