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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.accessioned2007-09-20T11:52:56Z
dc.date.available2007-09-20T11:52:56Z
dc.date.issued1998
dc.identifier.issn1134-5632
dc.identifier.urihttp://hdl.handle.net/2099/3535
dc.description.abstractIn previous works, we have presented two methodologies to obtain fuzzy rules in order to describe the behaviour of a system. We have used Artificial Neural Netorks (ANN) with the {\it Backpropagation} algorithm, and a set of examples of the system. In this work, some modifications which allow to improve the results, by means of an aptation or refinement of the variable labels in each rule, or the extraction of local rules using distributed ANN, are showed. An interesting application on the assignement of semantic to the classes obtained in a classification without previous classes process is also included.
dc.format.extent333-343
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 1998 Vol. 5 Núm. 2 [ -3 ]
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherArtificial neural netwoks
dc.subject.otherLearning
dc.subject.otherFuzzy rules
dc.subject.otherSemantic in classification prosseses
dc.subject.otherANN
dc.subject.otherBackpropagation algorithm
dc.titleNew aspects on extraction of fuzzy rules using neural networks
dc.typeArticle
dc.subject.lemacIntel·ligència artificial
dc.subject.lemacLògica difusa
dc.subject.lemacXarxes neuronals (Informàtica)
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.rights.accessOpen Access


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