Affine Takagi-Sugeno fuzzy model identification based on a novel fuzzy c-regression model clustering and particle swarm optimization

Author

Soltani, Moêz ; Bessaoudi, Talel ; Chaari, Abdelkader ; BenHmida, Fayçal

Author_Institution

High Sch. of Sci. & Tech. of Tunis, Tunis, Tunisia

fYear

2012

fDate

25-28 March 2012

Firstpage

1067

Lastpage

1070

Abstract

In this paper, a novel Takagi-Sugeno fuzzy model identification based on a new fuzzy c-regression model clustering algorithm and particle swarm optimization is presented. The main motivation for this work is to develop an identification procedure for nonlinear systems taking into account the noise. In addition, a new distance is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Thereafter, particle swarm optimization is employed to fine tune parameters of the obtained fuzzy model. The performance of the proposed approach is validated by studying the nonlinear plant modeling problem.

Keywords

fuzzy control; identification; nonlinear control systems; particle swarm optimisation; pattern clustering; regression analysis; FCRM algorithm; Takagi-Sugeno fuzzy model identification; fuzzy c-regression model clustering; nonlinear plant modeling problem; nonlinear system; particle swarm optimization; Clustering algorithms; Computational modeling; Data models; Noise; Particle swarm optimization; Robustness; Simulation;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrotechnical Conference (MELECON), 2012 16th IEEE Mediterranean

Conference_Location

Yasmine Hammamet

ISSN

2158-8473

Print_ISBN

978-1-4673-0782-6

Type

conf

DOI

10.1109/MELCON.2012.6196612

Filename

6196612