DocumentCode :
420577
Title :
A new fuzzy modeling and identification based on fast-cluster and genetic algorithm
Author :
Liu, Fucai ; Lu, Pingli ; Pei, Run
Author_Institution :
Dept. of Autom., Yanshan Univ., Qin-Huangdao, China
Volume :
1
fYear :
2004
fDate :
15-19 June 2004
Firstpage :
290
Abstract :
A new fuzzy identification algorithm is proposed in this paper, which include five blocks: input variables partition block, fast-cluster block, genetic algorithm block, tuning block and termination block. Fast-cluster block is to identify antecedent parameters of T-S model speedily. Tuning block is to fine-tune the parameters of T-S model using the gradient descent approach and termination block checks if the result is satisfactory. The proposed algorithm not only has the advantage of simplicity, but also has high accuracy, strong automation. The simulations indicate that the algorithm is effective in constructing T-S model for complex nonlinear systems.
Keywords :
fuzzy set theory; genetic algorithms; gradient methods; identification; modelling; nonlinear control systems; T-S model; complex nonlinear control systems; fast cluster block; fuzzy identification algorithm; fuzzy modeling; genetic algorithm block; gradient descent approach; tuning block; Automation; Control engineering; Fuzzy control; Genetic algorithms; Input variables; Nonlinear systems; Partitioning algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
Type :
conf
DOI :
10.1109/WCICA.2004.1340576
Filename :
1340576
Link To Document :
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