Neural approach for solving several types of optimization problems

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dc.contributor.authorda Silva, I. N.
dc.contributor.authorAmaral, W. C.
dc.contributor.authorArruda, L. V. R.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionFed Ctr Educ Technol
dc.date.accessioned2014-05-20T15:28:36Z
dc.date.available2014-05-20T15:28:36Z
dc.date.issued2006-03-01
dc.description.abstractNeural networks consist of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of recurrent neural net-works that can be used to solve several classes of optimization problems. More specifically, a modified Hopfield network is developed and its inter-nal parameters are computed explicitly using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points, which represent a solution of the problem considered. The problems that can be treated by the proposed approach include combinatorial optimiza-tion problems, dynamic programming problems, and nonlinear optimization problems.en
dc.description.affiliationState Univ São Paulo, Dept Elect Engn, Bauru, SP, Brazil
dc.description.affiliationUniv Estadual Campinas, Dept Comp Engn, Campinas, SP, Brazil
dc.description.affiliationFed Ctr Educ Technol, CEFET PR, CPGEI, Curitiba, Parana, Brazil
dc.description.affiliationUnespState Univ São Paulo, Dept Elect Engn, Bauru, SP, Brazil
dc.format.extent563-580
dc.identifierhttp://dx.doi.org/10.1007/s10957-006-9032-9
dc.identifier.citationJournal of Optimization Theory and Applications. New York: Springer/plenum Publishers, v. 128, n. 3, p. 563-580, 2006.
dc.identifier.doi10.1007/s10957-006-9032-9
dc.identifier.issn0022-3239
dc.identifier.urihttp://hdl.handle.net/11449/38376
dc.identifier.wosWOS:000241554100005
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofJournal of Optimization Theory and Applications
dc.relation.ispartofjcr1.234
dc.relation.ispartofsjr0,813
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectrecurrent neural networkspt
dc.subjectnonlinear optimizationpt
dc.subjectdynamic programmingpt
dc.subjectcombinatorial optimizationpt
dc.subjectHopfield networkpt
dc.titleNeural approach for solving several types of optimization problemsen
dc.typeArtigo
dcterms.licensehttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dcterms.rightsHolderSpringer
unesp.author.orcid0000-0002-1296-5454[1]
unesp.author.orcid0000-0002-5704-8131[3]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Engenharia, Baurupt

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Artigos - Engenharia Elétrica - FEB