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dc.contributor.authorLin, Sheng-Fuuen_US
dc.contributor.authorCheng, Yi-Changen_US
dc.date.accessioned2014-12-08T15:48:22Z-
dc.date.available2014-12-08T15:48:22Z-
dc.date.issued2010-09-01en_US
dc.identifier.issn1349-4198en_US
dc.identifier.urihttp://hdl.handle.net/11536/32218-
dc.description.abstractThis paper proposes a two-strategy reinforcement evolutionary algorithm using data-mining crossover strategy (TSR-EADCS) with a TSK-type fuzzy controller (TFC) for solving various control problems. The purpose of the R-EA DCS is not only to improve the design of traditional reinforcement signal but also to determine the suitable rules in a TEC and the suitable groups that are selected to perform crossover operation. Therefore, this paper proposes a two-strategy reinforcement signal to improve the performance of the traditional reinforcement signal design and uses the data mining technique to find suitable fuzzy rules and groups for evolution. The proposed TSR-EADCS consists of both structure and parameter learning. Ins fracture learning, the TSR-EADCS uses the self adaptive method to determine the suitability of TEC models between different numbers of fuzzy rules. In parameter learning, the TSR-EADCS uses the data-mining crossover strategy which is based on frequent pattern growth to select the suitable groups that are used to perform crossover operation. Illustrative examples are conducted to show the performance and applicability of the TSR-EADCS.en_US
dc.language.isoen_USen_US
dc.subjectFuzzy systemen_US
dc.subjectControlen_US
dc.subjectSymbiotic evolutionen_US
dc.subjectReinforcement learningen_US
dc.subjectFP-growthen_US
dc.titleTWO-STRATEGY REINFORCEMENT EVOLUTIONARY ALGORITHM USING DATA-MINING BASED CROSSOVER STRATEGY WITH TSK-TYPE FUZZY CONTROLLERSen_US
dc.typeArticleen_US
dc.identifier.journalINTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROLen_US
dc.citation.volume6en_US
dc.citation.issue9en_US
dc.citation.spage3863en_US
dc.citation.epage3885en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000281745700005-
dc.citation.woscount4-
Appears in Collections:Articles