Title :
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
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;
Conference_Titel :
Electrical Engineering and Software Applications (ICEESA), 2013 International Conference on
Conference_Location :
Hammamet
Print_ISBN :
978-1-4673-6302-0
DOI :
10.1109/ICEESA.2013.6578425
