Fuzzy Identification Based on a Chaotic Particle Swarm Optimization Approach Applied to a Nonlinear Yo-yo Motion System
dos Santos Coelho, L.
Herrera, B.M.
Pontifical Catholic Univ. of Parana, Curitiba;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Dec. 2007
Volume: 54,
Issue: 6
On page(s): 3234-3245
ISSN: 0278-0046
INSPEC Accession Number: 9692645
Digital Object Identifier: 10.1109/TIE.2007.896500
Current Version Published: 2007-11-12
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.
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