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dc.contributor.authorBlanco Morón, Armando
dc.contributor.authorDelgado Calvo-Flores, Miguel
dc.contributor.authorRequena Ramos, Ignacio
dc.date.accessioned2007-03-05T17:51:43Z
dc.date.available2007-03-05T17:51:43Z
dc.date.issued1994
dc.identifier.issn1134-5632
dc.identifier.urihttp://hdl.handle.net/2099/2461
dc.description.abstractThe Relational Equations approach is one of the most usual ones for describing (Fuzzy) Systems and in most cases, it is the final expression for other descriptions. This is why the identification of Relational Equations from a set of examples has received considerable atention in the specialized literature. This paper is devoted to this topic, more specifically to the topic of max-min neural networks for identification. Three methods of "learning" Fuzzy Systems are developed by combining the most desirable properties of two existing ones: Sayto-Mukaidono's technique and the so called "smoothed derivative" technique.
dc.format.extent11
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 1994 Vol. 1 Núm. 3 p.335-345
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherFuzzy relational equations
dc.subject.otherMax-min neural networks
dc.titleMax-Min fuzzy neural networks for solving relational equations
dc.typeArticle
dc.subject.lemacSistemes autoorganitzatius
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacIntel·ligència artificial
dc.subject.lemacBases de dades relacionals
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.rights.accessOpen Access
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