An improved electromagnetism-like algorithm for recurrent neural fuzzy controller design

Ching-Hung Lee*, Fu Kai Chang, Yu Chia Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)280-290
Number of pages11
JournalInternational Journal of Fuzzy Systems
Volume12
Issue number4
StatePublished - Dec 2010

Keywords

  • Electromagnetism-like algorithm
  • Fuzzy neural system
  • Genetic algorithm
  • Mobile robot
  • Nonlinear control

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