Recurrent fuzzy network design using hybrid evolutionary learning algorithms

Chia Feng Juang, I. Fang Chung*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


This paper proposes a recurrent fuzzy network design using the hybridization of a multigroup genetic algorithm and particle swarm optimization (R-MGAPSO). The recurrent fuzzy network designed here is the Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN), in which each fuzzy rule comprises spatial and temporal sub-rules. Both the number of fuzzy rules and the parameters in a TRFN are designed simultaneously by R-MGAPSO. In R-MGAPSO, the techniques of variable-length individuals and the local version of particle swarm optimization are incorporated into a genetic algorithm, where individuals with the same length constitute the same group, and there are multigroups in a population. Population evolution consists of three major operations: elite enhancement by particle swarm optimization, sub-rule alignment-based crossover, and mutation. To verify the performance of R-MGAPSO, dynamic plant and a continuous-stirred tank reactor controls are simulated. R-MGAPSO performance is also compared with genetic algorithms in these simulations.

Original languageEnglish
Pages (from-to)3001-3010
Number of pages10
Issue number16-18
StatePublished - Oct 2007


  • Continuous-stirred tank reactor control
  • Dynamic plant control
  • Genetic algorithms
  • Particle swam optimization
  • Recurrent fuzzy neural networks
  • Structure/parameter learning


Dive into the research topics of 'Recurrent fuzzy network design using hybrid evolutionary learning algorithms'. Together they form a unique fingerprint.

Cite this