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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/70007
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dc.contributor.authorDa Silva, Ivan Nunes-
dc.contributor.authorFlauzino, Rogério Andrade-
dc.date.accessioned2014-05-27T11:22:39Z-
dc.date.accessioned2016-10-25T18:24:35Z-
dc.date.available2014-05-27T11:22:39Z-
dc.date.available2016-10-25T18:24:35Z-
dc.date.issued2007-12-01-
dc.identifierhttp://dx.doi.org/10.1007/978-3-540-73007-1_49-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 4507 LNCS, p. 399-406.-
dc.identifier.issn0302-9743-
dc.identifier.issn1611-3349-
dc.identifier.urihttp://hdl.handle.net/11449/70007-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/70007-
dc.description.abstractThis paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology. © Springer-Verlag Berlin Heidelberg 2007.en
dc.format.extent399-406-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectFuzzy systems-
dc.subjectSystem optimization-
dc.subjectTuning algorithm-
dc.subjectComputer simulation-
dc.subjectConstrained optimization-
dc.subjectError analysis-
dc.subjectParameter estimation-
dc.subjectTime series analysis-
dc.subjectFuzzy inference-
dc.titleEfficient parametric adjustment of fuzzy inference system using unconstrained optimizationen
dc.typeoutro-
dc.contributor.institutionUniversidade de São Paulo (USP)-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationUniversity of São Paulo Department of Electrical Engineering, CP 359, CEP 13566.590, São Carlos, SP-
dc.description.affiliationSão Paulo State University Department of Production Engineering, CP 473, CEP 17033.360, Bauru, SP-
dc.description.affiliationUnespSão Paulo State University Department of Production Engineering, CP 473, CEP 17033.360, Bauru, SP-
dc.identifier.doi10.1007/978-3-540-73007-1_49-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.identifier.scopus2-s2.0-38049162135-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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