Hodjatollah Hamidi - Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
Atefeh Daraei - Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran

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 allopen 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 fuzzysystem 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 thenonlinear system model. The example used in this article is the Mackey-Glass Chaotic System on which the proposed method is finally applied.

System Identification; Combined Training; Fuzzy Rules; Database Design