Reinforcement evolutionary learning using data mining algorithm with TSK-type fuzzy controllers

Chi Yao Hsu, Yung Chi Hsu, Sheng-Fuu Lin*

*Corresponding author for this work

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Reinforcement evolutionary learning using data mining algorithm (R-ELDMA) with a TSK-type fuzzy controller (TFC) for solving reinforcement control problems is proposed in this study. R-ELDMA aims to determine suitable rules in a TFC and identify suitable and unsuitable groups for chromosome selection. To this end, the proposed R-ELDMA entails both structure and parameter learning. In structure learning, the proposed R-ELDMA adopts our previous research - the self-adaptive method (SAM) - to determine the suitability of TFC models with different fuzzy rules. In parameter learning, the data-mining based selection strategy (DSS), which proposes association rules, is used. More specifically, DSS not only determines suitable groups for chromosomes selection but also identifies unsuitable groups to be avoided selecting chromosomes to construct a TFC. Illustrative examples are presented to show the performance and applicability of the proposed R-ELDMA.

Original languageEnglish
Pages (from-to)3247-3259
Number of pages13
JournalApplied Soft Computing Journal
Volume11
Issue number3
DOIs
StatePublished - 1 Apr 2011

Keywords

  • Association rules
  • Control
  • Fuzzy system
  • R-ELDMA
  • Reinforcement learning
  • Symbiotic evolution

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