Nonlinear fuzzy neural controller design via em-based hybrid algorithm

Ching Hung Lee*, Yu Chia Lee


研究成果: Article同行評審


In this paper, we propose a hybridization of electromagnetism-like (EM) algorithm and particle swarm optimization (PSO) method to design recurrent fuzzy neural systems for nonlinear control. The hybrid algorithm (called modified EMPSO) combines the advantages of EM and PSO algorithms to enhance the performance of optimization. The main modification from EM algorithm is the random neghborhood local search is replaced by PSO algorithm with an instant update strategy. Each particle’s velocity is updated instantaneously and it provides the best information for other particles. Thus, it enhances the convergence speed and the computational efficiency. Simulation results of nonlinear systems control and two-degree-of-freedom helicopter system are shown to illustrate the modified EMPSO has the ability of global optimization, faster convergence, and higher accuracy.

頁(從 - 到)101-116
期刊International Journal of Computational Intelligence in Control
出版狀態Published - 2020


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