NEUROCOMPUTATIONAL APPROACH TO SOLVE A CONVEXLY COMBINED FUZZY RELATIONAL EQUATION WITH GENERALIZED CONNECTIVES

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dc.contributor.authorCHUN, MGko
dc.contributor.authorBien, Zeung namko
dc.date.accessioned2013-02-24T08:54:22Z-
dc.date.available2013-02-24T08:54:22Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued1993-08-
dc.identifier.citationFUZZY SETS AND SYSTEMS, v.57, no.3, pp.321 - 333-
dc.identifier.issn0165-0114-
dc.identifier.urihttp://hdl.handle.net/10203/55869-
dc.description.abstractIn this paper, we present a method to solve a convexly combined fuzzy relational equation with generalized connectives. For this, we propose a neural network whose structure represents the fuzzy relational equation. Then we derive a learning algorithm by using the concept of back-propagation learning. Since the proposed method can be used for a general form of fuzzy relational equations, such fuzzy max-min or min-max relational equations can be treated as its special cases.-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE BV-
dc.subjectINVERSE PROBLEM-
dc.subjectSYSTEMS-
dc.subjectIDENTIFICATION-
dc.titleNEUROCOMPUTATIONAL APPROACH TO SOLVE A CONVEXLY COMBINED FUZZY RELATIONAL EQUATION WITH GENERALIZED CONNECTIVES-
dc.typeArticle-
dc.identifier.wosidA1993LW63900004-
dc.identifier.scopusid2-s2.0-0027647229-
dc.type.rimsART-
dc.citation.volume57-
dc.citation.issue3-
dc.citation.beginningpage321-
dc.citation.endingpage333-
dc.citation.publicationnameFUZZY SETS AND SYSTEMS-
dc.contributor.localauthorBien, Zeung nam-
dc.contributor.nonIdAuthorCHUN, MG-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorFUZZY RELATIONAL EQUATIONS-
dc.subject.keywordAuthorNEURAL NETWORKS-
dc.subject.keywordAuthorIDENTIFICATION OF FUZZY SYSTEMS-
dc.subject.keywordPlusINVERSE PROBLEM-
dc.subject.keywordPlusSYSTEMS-
dc.subject.keywordPlusIDENTIFICATION-
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