Reinforcement hybrid evolutionary learning for recurrent wavelet-based neurofuzzy systems

Cheng-Jian Lin, Yung-Chia Hsu

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

This paper proposes a recurrent wavelet-based neurofuzzy system (RWNFS) with the reinforcement hybrid evolutionary learning algorithm (R-HELA) for solving various control problems. The proposed R-HELA combines the compact genetic algorithm (CGA), and the modified variable-length genetic algorithm (MVGA) performs the structure/parameter learning for dynamically constructing the RWNFS. That is, both the number of rules and the adjustment of parameters in the RWNFS are designed concurrently by the R-HELA. In the R-HELA, individuals of the same length constitute the same group. There are multiple groups in a population. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. Illustrative examples were conducted to show the performance and applicability of the proposed R-HELA method.
Original languageEnglish
Pages (from-to)729-745
Number of pages17
JournalIEEE Transactions on Fuzzy Systems
Volume15
Issue number4
DOIs
StatePublished - Aug 2007

Keywords

  • control
  • genetic algorithms
  • neurofuzzy system
  • recurrent network
  • reinforcement
  • FUZZY INFERENCE NETWORK
  • GENETIC ALGORITHM
  • NEURAL-NETWORKS
  • CLASSIFICATION PROBLEMS
  • KNOWLEDGE EXTRACTION;
  • SYMBIOTIC EVOLUTION
  • DESIGN
  • RULES
  • IDENTIFICATION
  • CONTROLLERS

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