Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44379
DC FieldValueLanguage
dc.contributor.authorJuang, C.F.en_US
dc.contributor.author莊家峰zh_TW
dc.contributor.authorLee, C.I.en_US
dc.date2007zh_TW
dc.date.accessioned2014-06-06T08:12:13Z-
dc.date.available2014-06-06T08:12:13Z-
dc.identifier.issn0925-2312zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/44379-
dc.description.abstractA fuzzified Takagi-Sugeno-Kang (TSK)-type neural fuzzy inference network (FTNFIN) that is capable of handling both linguistic and numerical information simultaneously is proposed in this paper. FTRNFN solves the disadvantages of most existing neural fuzzy systems which can only handle numerical information. The inputs and outputs of FTNFIN may be fuzzy numbers with any shapes or numerical values. Structurally, FTNFIN is a fuzzy network constructed from a series of fuzzy if-then rules with TSK-type consequent parts. The alpha-cut technique is used in input fuzzification and consequent part computation, which enables the network to simultaneously handle both numerical and linguistic information. There are no rules in FTNFIN initially since they are constructed on-line by concurrent structure and parameter learning. FTNFIN is characterized by small network size and high learning accuracy, and can be applied to linguistic information processing. The network has been applied to the learning of fuzzy if-then rules, a mathematical function with fuzzy inputs and outputs, and truck backing control problem. Good simulation results are achieved from all these applications. (C) 2007 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USzh_TW
dc.relationNeurocomputingen_US
dc.relation.ispartofseriesNeurocomputing, Volume 71, Issue 1-3, Page(s) 342-352.en_US
dc.relation.urihttp://dx.doi.org/10.1016/j.neucom.2006.12.020en_US
dc.subjectTSK-type fuzzy rulesen_US
dc.subjectfuzzy neural networken_US
dc.subjectstructure/parameteren_US
dc.subjectlearningen_US
dc.subjectalpha-cuten_US
dc.subjectlinguistic informationen_US
dc.subjecttruck backing controlen_US
dc.subjectsystemsen_US
dc.subjectidentificationen_US
dc.titleA fuzzified neural fuzzy inference network for handling both linguistic and numerical information simultaneouslyen_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1016/j.neucom.2006.12.020zh_TW
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