Fuzzy c-regression models based on Euclidean particle swarm optimization

This paper proposes a modified fuzzy c-regression models clustering algorithm based on Euclidean particle swarm optimization. The Fuzzy C-Regression Models (FCRM) clustering algorithm has a considerable sensitive to initialization susceptible to converge to a local minimum of the objective function. In order to overcome this problem, Euclidean particle swarm optimization is employed to optimize the initial states of FCRM. The orthogonal least squares is used to identify the unknown parameters of local linear model. Finally, numerical example are given to verify the effectiveness of the proposed approach.