Article


Article Code : 13951025947524609(DOI : 10.7508/jist.2016.04.002)

Article Title : Identification of a Nonlinear System by Determining of Fuzzy Rules

Journal Number : 16 Autumn 2016

Visited : 830

Files : 507 KB


List of Authors

  Full Name Email Grade Degree Corresponding Author
1 Hodjat Hamidi h_hamidi@kntu.ac.ir Associate Professor PhD
2 Atefeh Daraei adaraei@mail.kntu.ac.ir - PhD

Abstract

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.