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dc.contributor.authorLin, Sheng-Fuuen_US
dc.contributor.authorChang, Jyun-Weien_US
dc.contributor.authorHsu, Yung-Chien_US
dc.date.accessioned2014-12-08T15:21:54Z-
dc.date.available2014-12-08T15:21:54Z-
dc.date.issued2012-03-01en_US
dc.identifier.issn0924-669Xen_US
dc.identifier.urihttp://dx.doi.org/10.1007/s10489-010-0271-yen_US
dc.identifier.urihttp://hdl.handle.net/11536/15591-
dc.description.abstractIn this paper, a self-organization mining based hybrid evolution (SOME) learning algorithm for designing a TSK-type fuzzy model (TFM) is proposed. In the proposed SOME, group-based symbiotic evolution (GSE) is adopted in which each group in the GSE represents a collection of only one fuzzy rule. The proposed SOME consists of structure learning and parameter learning. In structure learning, the proposed SOME uses a two-step self-organization algorithm to decide the suitable number of rules in a TFM. In parameter learning, the proposed SOME uses the data mining based selection strategy and data mining based crossover strategy to decide groups and parental groups by the data mining algorithm that called frequent pattern growth. Illustrative examples were conducted to verify the performance and applicability of the proposed SOME method.en_US
dc.language.isoen_USen_US
dc.subjectGenetic algorithmen_US
dc.subjectFuzzy modelen_US
dc.subjectGroup-based symbiotic evolutionen_US
dc.subjectData miningen_US
dc.subjectIdentificationen_US
dc.subjectFP-Growthen_US
dc.titleA self-organization mining based hybrid evolution learning for TSK-type fuzzy model designen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s10489-010-0271-yen_US
dc.identifier.journalAPPLIED INTELLIGENCEen_US
dc.citation.volume36en_US
dc.citation.issue2en_US
dc.citation.spage454en_US
dc.citation.epage471en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000300657400013-
dc.citation.woscount1-
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