Training matrix parameters by Particle Swarm Optimization using a fuzzy neural network for identification

In this article Particle Swarm Optimization that is a population-based method is applied to train the matrix parameters that are standard deviation and centers of Radial Basis Function Fuzzy Neural Network. We have applied Least Square and Recursive Least Square in training the weights of this fuzzy neural network .There are four sets of data used to examine and prove that Particle Swarm Optimization is a good method for training these complicated matrices as antecedent part parameters.