DocumentCode :
976215
Title :
Fuzzy Identification Based on a Chaotic Particle Swarm Optimization Approach Applied to a Nonlinear Yo-yo Motion System
Author :
Coelho, Leandro Dos Santos ; Herrera, Bruno Meirelles
Author_Institution :
Pontifical Catholic Univ. of Parana, Curitiba
Volume :
54
Issue :
6
fYear :
2007
Firstpage :
3234
Lastpage :
3245
Abstract :
The identification of uncertain and nonlinear systems is an important and challenging problem. Fuzzy models, particularly Takagi-Sugeno (TS), have received particular attention in the area of nonlinear identification due to their potentialities to approximate any nonlinear behavior. A method of nonlinear identification based on the TS fuzzy model and optimization procedure is proposed in this paper. Chaotic particle swarm optimization (CPSO) algorithms, based on chaotic Zaslavskii map sequences, combined with efficient Gustafson-Kessel (GK) clustering algorithm are proposed here for the design of the premise part of production rules, while the least-mean-square technique is utilized for the subsequent part of the production rules of the TS fuzzy model. An experimental case study using a nonlinear yo-yo motion control system is analyzed by the proposed algorithms. The numerical results presented here indicate that the traditional particle swarm optimization algorithm and, particularly, the CPSO combined with GK algorithms are effective in building a good TS fuzzy model for nonlinear identification.
Keywords :
chaos; fuzzy control; least mean squares methods; motion control; nonlinear control systems; particle swarm optimisation; statistical analysis; uncertain systems; Gustafson-Kessel clustering algorithm; Takagi-Sugeno model; chaotic Zaslavskii map sequences; chaotic particle swarm optimization approach; fuzzy identification; least-mean-square technique; nonlinear yo-yo motion control system; uncertain system; Algorithm design and analysis; Chaos; Clustering algorithms; Fuzzy systems; Motion control; Nonlinear systems; Optimization methods; Particle production; Particle swarm optimization; Takagi-Sugeno model; Chaotic map; clustering algorithm; fuzzy identification; nonlinear systems; optimization; particle swarm optimization; system identification;
fLanguage :
English
Journal_Title :
Industrial Electronics, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0046
Type :
jour
DOI :
10.1109/TIE.2007.896500
Filename :
4383269
Link To Document :
بازگشت