A comparison of PSO and GA combined with LS and RLS in identification using fuzzy gaussian neural networks

In this article, a new method for training the parameters is discussed and we have compared the function of particle swarm optimization with genetic algorithm in training the standard deviation and centers in the antecedent part of fuzzy gaussian neural network. We have applied least square and recursive least square in training the weights of this fuzzy neural network in the conclusion part. There are four sets of data used to examine the proposed learning strategy to achieve the proper learning mode.