Nonlinear systems design by a novel fuzzy neural system via hybridization of electromagnetism-like mechanism and particle swarm optimisation algorithms

  • Authors:

  • Ching-Hung Lee;Yu-Chia Lee

  • Affiliations:

  • Department of Electrical Engineering, Yuan Ze University, Chung-li, Taoyuan 320, Taiwan;Department of Electrical Engineering, Yuan Ze University, Chung-li, Taoyuan 320, Taiwan

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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Abstract

In this paper, a hybrid of algorithms for electromagnetism-like mechanisms (EM) and particle swarm optimisation (PSO), called HEMPSO, is proposed for use in designing a functional-link-based Petri recurrent fuzzy neural system (FLPRFNS) for nonlinear system control. The FLPRFNS has a functional link-based orthogonal basis function fuzzy consequent and a Petri layer to eliminate the redundant fuzzy rule for each input calculation. In addition, the FLPRFNS is trained by the proposed hybrid algorithm. The main innovation is that the random-neighbourhood local search is replaced by a PSO algorithm with an instant-update strategy for particle information. Each particle updates its information instantaneously and in this way receives the best current information. Thus, HEMPSO combines the advantages of multiple-agent-based searching, global optimisation, and rapid convergence. Simulation results confirm that HEMPSO can be used to perform global optimisation and offers the advantage of rapid convergence; they also indicate that the FLPRFNS exhibits high accuracy.