A fast learning algorithm for evolving neo-fuzzy neuron

dc.creatorSilva, AM
dc.creatorCaminhas, W
dc.creatorLemos, A
dc.creatorGomide, F
dc.date2014
dc.dateJAN
dc.date2014-07-30T13:39:39Z
dc.date2015-11-26T16:36:56Z
dc.date2014-07-30T13:39:39Z
dc.date2015-11-26T16:36:56Z
dc.date.accessioned2018-03-28T23:19:51Z
dc.date.available2018-03-28T23:19:51Z
dc.identifierApplied Soft Computing. Elsevier Science Bv, v. 14, n. 194, n. 209, 2014.
dc.identifier1568-4946
dc.identifier1872-9681
dc.identifierWOS:000327528300006
dc.identifier10.1016/j.asoc.2013.03.022
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/53155
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/53155
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1271931
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionThis paper introduces an evolving neural fuzzy modeling approach constructed upon the neo-fuzzy neuron and network. The approach uses an incremental learning scheme to simultaneously granulatethe input space and update the neural network weights. The neural network structure and parameters evolve simultaneously as data are input. Initially the space of each input variable is granulated using two complementary triangular membership functions. New triangular membership functions may be added, excluded and/or have their parameters adjusted depending on the input data and modeling error. The parameters of the network are updated using a gradient-based scheme with optimal learning rate. The performance of the approach is evaluated using instances of times series forecasting and nonlinear system identification problems. Computational experiments and comparisons against alternative evolving models show that the evolving neural neo-fuzzy network is accurate and fast, characteristics which are essential for adaptive systems modeling, especially in real-time, on-line environments. (C) 2013 Elsevier B. V. All rights reserved.
dc.description14
dc.descriptionB
dc.description194
dc.description209
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionBrazilian Minister of Education and Innovation
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationApplied Soft Computing
dc.relationAppl. Soft. Comput.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectEvolving neural fuzzy systems
dc.subjectNeo-fuzzy neuron
dc.subjectAdaptive modeling
dc.subjectInference System
dc.subjectData Streams
dc.subjectOnline Identification
dc.subjectProcess Parameters
dc.subjectModels
dc.subjectClassifiers
dc.subjectClassification
dc.subjectController
dc.subjectPrediction
dc.subjectFlexfis
dc.titleA fast learning algorithm for evolving neo-fuzzy neuron
dc.typeArtículos de revistas


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