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dc.contributor.authorLin, Chin-Tengen_US
dc.contributor.authorHan, Ming-Fengen_US
dc.contributor.authorLin, Yang-Yinen_US
dc.contributor.authorLiao, Shih-Huien_US
dc.contributor.authorChang, Jyh-Yeongen_US
dc.date.accessioned2014-12-08T15:20:29Z-
dc.date.available2014-12-08T15:20:29Z-
dc.date.issued2011en_US
dc.identifier.isbn978-1-4244-7317-5en_US
dc.identifier.issn1098-7584en_US
dc.identifier.urihttp://hdl.handle.net/11536/14581-
dc.description.abstractThis paper proposes a differential evolution with local information for TSK-type neuro-fuzzy system optimization. The differential evolution with local information consider neighborhood between each individual to keep the diversity of population. An adaptive parameter tuning based on 1/5th rule is used to trade off between local search and global search. For structure learning algorithm, the on-line clustering algorithm is used for rule generation. The structure learning algorithm generates a new rule which compares the firing strength. Initially, there is no rule in neuro-fuzzy system model. The rules are automatically generated by fuzzy measure. For parameter learning, the parameters are optimized by differential evolution algorithm. Finally, the proposed neuro-fuzzy system with novel differential evolution model is applied in chaotic sequence prediction problem. Results of this paper demonstrate the effectiveness of the proposed model.en_US
dc.language.isoen_USen_US
dc.subjectEvolution Algorithmen_US
dc.subjectNeuro-Fuzzy Systemen_US
dc.subjectFuzzy Systemen_US
dc.subjectDifferential Evolution Optimizationen_US
dc.titleNeuro-Fuzzy System Design Using Differential Evolution with Local Informationen_US
dc.typeProceedings Paperen_US
dc.identifier.journalIEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)en_US
dc.citation.spage1003en_US
dc.citation.epage1006en_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:000295224300149-
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