An Improved Electromagnetism-like Algorithm for Recurrent Neural Fuzzy Controller Design

This paper introduces an improved electromagnetism-like algorithm (IEM) for recurrent fuzzy neural controller design. The hybrid IEM algorithm combines the advantages of the electromagnetism-like (EM) algorithm and the genetic algorithm (GA). The proposed IEM is composed of initialization, local search, total force calculation, movement, and evaluation. For recurrent fuzzy neural controller design, IEM simulates the ”attraction” and ”repulsion” of charged particles based on the electromagnetism theory by considering each fuzzy neural system as an electrical charge. IEM algorithm involves replacing the neighborhood randomly local search with a competitive concept and GA. IEM can treat the optimization of fuzzy neural systems for gradient information free systems. In addition, IEM has the capability of rapidly convergence and reduces the computation complexity of EM. Finally, two illustrative examples are presented to demonstrate the performance and effectiveness of IEM.

並列關鍵字

Electromagnetism-like algorithm ； genetic algorithm ； fuzzy neural system ； nonlinear control ； mobile robot

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An Improved Electromagnetism-like Algorithm for Recurrent Neural Fuzzy Controller Design

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