A new objective function for fuzzy c-regression model and its application to T-S fuzzy model identification

2011 ◽  
Author(s):  
Moez Soltani ◽  
Abdelkader Chaari ◽  
Faycal BenHmida ◽  
Moncef Gossa
Keyword(s):  
Regression Model ◽  
Objective Function ◽  
Model Identification ◽  
Fuzzy Model

scholarly journals A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization

2012 ◽  
Vol 22 (3) ◽  
pp. 617-628 ◽  
Author(s):  
Moêz Soltani ◽  
Abdelkader Chaari ◽  
Fayçal Ben Hmida
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Objective Function ◽  
Clustering Algorithm ◽  
Least Squares Method ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Unknown Parameters ◽  
Swarm Optimization ◽  
Error Measure

Abstract This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.

T–S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm

2009 ◽  
Vol 22 (4-5) ◽  
pp. 646-653 ◽  
Author(s):  
Chaoshun Li ◽  
Jianzhong Zhou ◽  
Xiuqiao Xiang ◽  
Qingqing Li ◽  
Xueli An
Keyword(s):  
Regression Model ◽  
Clustering Algorithm ◽  
Model Identification ◽  
Fuzzy Model
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