Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44345
DC FieldValueLanguage
dc.contributor.authorJuang, C.F.en_US
dc.contributor.author莊家峰zh_TW
dc.contributor.authorHsiao, C.M.en_US
dc.contributor.authorHsu, C.H.en_US
dc.date2010zh_TW
dc.date.accessioned2014-06-06T08:12:11Z-
dc.date.available2014-06-06T08:12:11Z-
dc.identifier.issn1063-6706zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/44345-
dc.description.abstractThis paper proposes a hierarchical cluster-based multispecies particle-swarm optimization (HCMSPSO) algorithm for fuzzy-system optimization. The objective of this paper is to learn Takagi-Sugeno-Kang (TSK) type fuzzy rules with high accuracy. In the HCMSPSO-designed fuzzy system (FS), each rule defines its own fuzzy sets, which implies that the number of fuzzy sets for each input variable is equal to the number of fuzzy rules. A swarm in HCMSPSO is clustered into multiple species at an upper hierarchical level, and each species is further clustered into multiple subspecies at a lower hierarchical level. For an FS consisting of r rules, r species (swarms) are formed in the upper level, where one species optimizes a single fuzzy rule. Initially, there are no species in HCMSPSO. An online cluster-based algorithm is proposed to generate new species (fuzzy rules) automatically. In the lower layer, subspecies within the same species are formed adaptively in each iteration during the particle update. Several simulations are conducted to verify HCMSPSO performance. Comparisons with other neural learning, genetic, and PSO algorithms demonstrate the superiority of HCMSPSO performance.en_US
dc.language.isoen_USzh_TW
dc.relationIeee Transactions on Fuzzy Systemsen_US
dc.relation.ispartofseriesIeee Transactions on Fuzzy Systems, Volume 18, Issue 1, Page(s) 14-26.en_US
dc.relation.urihttp://dx.doi.org/10.1109/tfuzz.2009.2034529en_US
dc.subjectFuzzy modelingen_US
dc.subjectfuzzy predictionen_US
dc.subjectparticle-swarm optimization (PSO)en_US
dc.subjectswarm intelligenceen_US
dc.subjectmultiple data setsen_US
dc.subjectstatistical comparisonsen_US
dc.subjectgenetic algorithmen_US
dc.subjectneural-networken_US
dc.subjectconvergenceen_US
dc.subjectdesignen_US
dc.subjectidentificationen_US
dc.subjectclassifiersen_US
dc.subjectpredictionen_US
dc.subjectstrategyen_US
dc.titleHierarchical Cluster-Based Multispecies Particle-Swarm Optimization for Fuzzy-System Optimizationen_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1109/tfuzz.2009.2034529zh_TW
item.openairetypeJournal Article-
item.fulltextno fulltext-
item.grantfulltextnone-
item.languageiso639-1en_US-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
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