Fuzzy c-regression models based on the BELS method for nonlinear system identification

Author

Aissaoui, Borhen ; Soltani, Mahdi ; Elleuch, Dorsaf ; Chaari, Abdelkader

Author_Institution

Res. Unit on Control, Monitoring & Safety of Syst. (C3S), ESSTT, Tunis, Tunisia

fYear

2013

fDate

21-23 March 2013

Firstpage

1

Lastpage

6

Abstract

A fuzzy c-regression model clustering algorithm based on Bias-Eliminated Least Squares method (BELS) is presented. This method is designed to develop an identification procedure for noisy nonlinear systems. The BELS method is used to identify consequent parameters and eliminate the bias. The proposed approach has been applied to benchmark modeling problem which proved a good performance.

Keywords

fuzzy set theory; least squares approximations; nonlinear systems; parameter estimation; pattern clustering; regression analysis; BELS method; benchmark modeling problem; bias-eliminated least squares method; fuzzy c-regression model clustering algorithm; noisy nonlinear system identification procedure; parameter identification; Clustering algorithms; Computational modeling; Equations; Mathematical model; Noise; Nonlinear systems; Vectors; Bias-Eliminated Least-Squares; Takagi-Sugeno fuzzy model; fuzzy c-regression models;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Engineering and Software Applications (ICEESA), 2013 International Conference on

Conference_Location

Hammamet

Print_ISBN

978-1-4673-6302-0

Type

conf

DOI

10.1109/ICEESA.2013.6578425

Filename

6578425