Reinforcement group cooperation-based symbiotic evolution for recurrent wavelet-based neuro-fuzzy systems

Yung Chi Hsu, Sheng-Fuu Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

This paper proposes a recurrent wavelet-based neuro-fuzzy system (RWNFS) with a reinforcement group cooperation-based symbiotic evolution (R-GCSE) for solving various control problems. The R-GCSE is different from the traditional symbiotic evolution. In the R-GCSE method, a population is divided to several groups. Each group formed by a set of chromosomes represents a fuzzy rule and cooperates with other groups to generate better chromosomes by using the proposed elite-based compensation crossover strategy (ECCS). In this paper, the proposed R-GCSE is used to evaluate numerical control problems. The performance of the R-GCSE in the simulations is excellent compared with other existing models.

Original languageEnglish
Pages (from-to)2418-2432
Number of pages15
JournalNeurocomputing
Volume72
Issue number10-12
DOIs
StatePublished - 1 Jun 2009

Keywords

  • Control
  • Neuro-fuzzy system
  • Recurrent network
  • Reinforcement learning
  • Symbiotic evolution

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