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dc.contributor.authorChen, Cheng-Hungen_US
dc.contributor.authorLin, Cheng-Jianen_US
dc.contributor.authorLin, Chin-Tengen_US
dc.date.accessioned2014-12-08T15:10:52Z-
dc.date.available2014-12-08T15:10:52Z-
dc.date.issued2008-10-01en_US
dc.identifier.issn1063-6706en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2008.924334en_US
dc.identifier.urihttp://hdl.handle.net/11536/8315-
dc.description.abstractThis study presents a functional-link-based neurofuzzy network (FLNFN) structure for nonlinear system control. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. Furthermore, results for the universal approximator and a convergence analysis of the FLNFN model are proven. Finally, the FLNFN model is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed FLNFN model.en_US
dc.language.isoen_USen_US
dc.subjectEntropyen_US
dc.subjectfunctional link neural networks (FLNNs)en_US
dc.subjectneurofuzzy networks (NFNs)en_US
dc.subjectnonlinear system controlen_US
dc.subjectonline learningen_US
dc.titleA Functional-Link-Based Neurofuzzy Network for Nonlinear System Controlen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TFUZZ.2008.924334en_US
dc.identifier.journalIEEE TRANSACTIONS ON FUZZY SYSTEMSen_US
dc.citation.volume16en_US
dc.citation.issue5en_US
dc.citation.spage1362en_US
dc.citation.epage1378en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000260046700020-
dc.citation.woscount37-
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