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

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 selectio...

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Bibliographic Details
Published in:Applied soft computing Vol. 11; no. 3; pp. 3247 - 3259
Main Authors: Hsu, Chi-Yao, Hsu, Yung-Chi, Lin, Sheng-Fuu
Format: Journal Article
Language:English
Published: Elsevier B.V 01.04.2011
Subjects:
Association rules
Control
Fuzzy system
R-ELDMA
Reinforcement learning
Symbiotic evolution
Symbiotic evolution
Fuzzy system
Control
Association rules
R-ELDMA
Reinforcement learning
ISSN:1568-4946, 1872-9681
Online Access:Get full text
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Summary: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.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2010.12.027