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

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Network Learning Technology > journal & Dissertation > Item 987654321/51595

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51595

Title:	Reinforcement evolutionary learning using data mining algorithm with TSK-type fuzzy controllers
Authors:	Hsu,CY;Hsu,YC;Lin,SF
Contributors:	網路學習科技研究所
Keywords:	SYMBIOTIC EVOLUTION;GENETIC ALGORITHMS;DESIGN;SYSTEMS;NETWORK;HYBRID;GA
Date:	2011
Issue Date:	2012-03-27 18:57:00 (UTC+8)
Publisher:	國立中央大學
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. Crown Copyright (C) 2010 Published by Elsevier B. V. All rights reserved.
Relation:	APPLIED SOFT COMPUTING
Appears in Collections:	[Graduate Institute of Network Learning Technology] journal & Dissertation

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