Nonlinear fuzzy neural controller design via em-based hybrid algorithm

Ching Hung Lee*, Yu Chia Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)101-116
Number of pages16
JournalInternational Journal of Computational Intelligence in Control
Issue number1
StatePublished - 2020


  • Electromagnetism-like algorithm
  • Fuzzy neural system
  • Hybrid
  • Nonlinear control
  • Particle swarm optimization

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