Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44003
DC FieldValueLanguage
dc.contributor.authorJuang, C.F.en_US
dc.contributor.author莊家峰zh_TW
dc.contributor.authorLo, C.en_US
dc.date2007zh_TW
dc.date.accessioned2014-06-06T08:11:47Z-
dc.date.available2014-06-06T08:11:47Z-
dc.identifier.issn0308-1079zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/44003-
dc.description.abstractThis paper proposes fuzzy systems design by clustering-aided ant colony optimization (ACO) algorithm (CACO). The objective of CACO is to improve both the design efficiency of fuzzy systems and its performance. In CACO, structure of a fuzzy system, including the number of rules and fuzzy sets in each input variable, is created on-line by a newly proposed fuzzy clustering. In contrast to conventional grid-type partition, the antecedent part of a fuzzy system is flexibly partitioned, and the phenomenon of highly overlapped fuzzy sets is avoided. Once a new rule is generated, the consequence is selected from a list of candidate control actions by ACO. In ACO, the route of an ant is regarded as a combination of consequent actions selected from every rule. A pheromone matrix among all candidate actions is constructed and an on-line learning algorithm for heuristic value update is proposed. Searching for the best one among all consequence combinations involves using the pheromone matrix and heuristic values. To verify the performance of CACO on fuzzy systems design, simulations on nonlinear system control, water bath temperature control and chaotic system control are performed. Simulations on these problems and comparisons with other algorithms have demonstrated the performance of CACO.en_US
dc.language.isoen_USzh_TW
dc.relationInternational Journal of General Systemsen_US
dc.relation.ispartofseriesInternational Journal of General Systems, Volume 36, Issue 6, Page(s) 623-641.en_US
dc.relation.urihttp://dx.doi.org/10.1080/03081070701288952en_US
dc.subjectant colony optimizationen_US
dc.subjectfuzzy controlen_US
dc.subjectneural fuzzy systemsen_US
dc.subjectstructureen_US
dc.subjectlearningen_US
dc.subjectgenetic fuzzy systemen_US
dc.subjectswarm intelligenceen_US
dc.subjectgenetic algorithmen_US
dc.subjectinference networken_US
dc.titleFuzzy systems design by clustering-aided ant colony optimization for plant controlen_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1080/03081070701288952zh_TW
item.openairetypeJournal Article-
item.fulltextno fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en_US-
Appears in Collections:電機工程學系所
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