identification of a nonlinear system by determining of fuzzy rules

In this article the hybrid optimization algorithm of differential evolution and particle swarm is introduced for designing the fuzzy rule base of a fuzzy controller. for a specific number of rules, a hybrid algorithm for optimizing all open parameters was used to reach maximum accuracy in training. the considered hybrid computational approach includes: opposition-based differential evolution algorithm and particle swarm optimization algorithm. to train a fuzzy system hich is employed for identification of a nonlinear system, the results show that the proposed hybrid algorithm approach demonstrates a better identification accuracy compared to other educational approaches in identification of the nonlinear system model. the example used in this article is the mackey-glass chaotic system on which the proposed method is finally applied.