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dc.contributor.authorBenítez Sánchez, José Manuel
dc.contributor.authorBlanco Morón, Armando
dc.contributor.authorDelgado Calvo-Flores, Miguel
dc.contributor.authorRequena Ramos, Ignacio
dc.date.accessioned2007-09-13T09:36:27Z
dc.date.available2007-09-13T09:36:27Z
dc.date.issued1996
dc.identifier.citationUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.identifier.issn1134-5632
dc.identifier.urihttp://hdl.handle.net/2099/3475
dc.description.abstractIn previous papers, we presented an empirical methodology based on Neural Networks for obtaining fuzzy rules which allow a system to be described, using a set of examples with the corresponding inputs and outputs. Now that the previous results have been completed, we present another procedure for obtaining fuzzy rules, also based on Neural Networks with Backpropagation, with no need to establish beforehand the labels or values of the variables that govern the system
dc.format.extent371-382
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 1996 Vol. 3 Núm. 3
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherObtaining fuzzy rules
dc.subject.otherLearning
dc.subject.otherArtificial neural networks
dc.titleNeural methods for obtaining fuzzy rules
dc.typeArticle
dc.subject.lemacLògica matemàtica
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS:: 03 Mathematical logic and foundations:: 03B General logic
dc.rights.accessOpen Access
local.personalitzacitaciotrue


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