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dc.contributor.authorHsu, Yung-Chien_US
dc.contributor.authorLin, Sheng-Fuuen_US
dc.date.accessioned2014-12-08T15:09:25Z-
dc.date.available2014-12-08T15:09:25Z-
dc.date.issued2009-06-01en_US
dc.identifier.issn0925-2312en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.neucom.2008.12.027en_US
dc.identifier.urihttp://hdl.handle.net/11536/7198-
dc.description.abstractThis paper proposes a recurrent wavelet-based neuro-fuzzy system (RWNFS) with a reinforcement group cooperation-based symbiotic evolution (R-GCSE) for solving various control problems. The R-GCSE is different from the traditional symbiotic evolution. In the R-GCSE method, a population is divided to several groups. Each group formed by a set of chromosomes represents a fuzzy rule and cooperates with other groups to generate better chromosomes by using the proposed elite-based compensation crossover strategy (ECCS). In this paper, the proposed R-GCSE is used to evaluate numerical control problems. The performance of the R-GCSE in the simulations is excellent compared with other existing models. (c) 2009 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectNeuro-fuzzy systemen_US
dc.subjectSymbiotic evolutionen_US
dc.subjectControlen_US
dc.subjectReinforcement learningen_US
dc.subjectRecurrent networken_US
dc.titleReinforcement group cooperation-based symbiotic evolution for recurrent wavelet-based neuro-fuzzy systemsen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.neucom.2008.12.027en_US
dc.identifier.journalNEUROCOMPUTINGen_US
dc.citation.volume72en_US
dc.citation.issue10-12en_US
dc.citation.spage2418en_US
dc.citation.epage2432en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000266702300039-
dc.citation.woscount3-
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