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Paper details
Number 3 - September 2012
Volume 22 - 2012
A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization
Moêz Soltani, Abdelkader Chaari, Fayçal Ben Hmida
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.
Keywords
Takagi–Sugeno fuzzy model, noise clustering algorithm, fuzzy c-regression model, orthogonal least squares, particle swarm optimization