Identification of a Nonlinear System by Determining of Fuzzy Rules
Journal Title: Journal of Information Systems and Telecommunication - Year 2016, Vol 4, Issue 4
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.
Authors and Affiliations
Hodjatollah Hamidi, Atefeh Daraei
Keywords
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- EP ID EP183936
- DOI 10.7508/jist.2016.04.002
- Views 162
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