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dc.contributor.authorFernández Garrido, José María
dc.contributor.authorRequena Ramos, Ignacio
dc.date.accessioned2007-09-27T09:20:49Z
dc.date.available2007-09-27T09:20:49Z
dc.date.issued2000
dc.identifier.issn1134-5632
dc.identifier.urihttp://hdl.handle.net/2099/3576
dc.description.abstractIn this paper, a methodology to obtain a set of fuzzy rules for classification systems is presented. The system is represented in a layered fuzzy network, in which the links from input to hidden nodes represents the antecedents of the rules, and the consequents are represented by links from hidden to output nodes. Specific genetic algorithms are used in two phases to extract the rules. In the first phase an initial version of the rules is extracted, and in second one, the labels are refined. The procedure is illustrated by applying it to two real-world classification problems
dc.format.extent185-197
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 2000 Vol. 7 Núm. 2 [ -3 ]
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherClassification Systems
dc.subject.otherSystems based on Fuzzy Rules
dc.subject.otherClassification Systems, Systems Genetic Algorithms.
dc.titleA methodology for constructing fuzzy rule based classification systems
dc.typeArticle
dc.subject.lemacProgramació (Matemàtica)
dc.subject.lemacSistemes experts (Informàtica)
dc.subject.amsClassificació AMS::90 Operations research, mathematical programming::90C Mathematical programming
dc.rights.accessOpen Access


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain