A Hybrid Algorithm of Electromagnetism-like and Genetic for Recurrent Neural Fuzzy Controller Design

Based on the electromagnetism-like algorithm (EM), we propose a novel hybrid learning algorithms which is the improved EM algorithm with genetic algorithm technique (IEMGA) for recurrent fuzzy neural system design. IEMGA are composed of initialization, local search, total force calculation, movement, and evaluation. They are hybridization of EM and GA. EM algorithm is a population-based meta-heuristic algorithm originated from the electromagnetism theory. For recurrent fuzzy neural system design, IEMGA simulates the "attraction" and "repulsion" of charged particles by considering each neural system parameters as an electrical charge. The modification from EM algorithm is the neighborhood randomly local search is replaced by GA and the competitive concept is adopted for IEMGA. For gradient information free system, IEMGA is proposed to treat the optimization problem. Besides, IEMGA consists of EM and GA to reduce the computation complexity of EM. IEMGA is used to develop the update laws of RFNN for nonlinear system control problem. Finally, several illustration examples are presented to show the performance and effectiveness of IEMGA.

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