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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/64990
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dc.contributor.authorTeixeira, Marcelo C M-
dc.contributor.authorZak, Stanislaw H.-
dc.date.accessioned2014-05-27T11:18:10Z-
dc.date.accessioned2016-10-25T18:14:15Z-
dc.date.available2014-05-27T11:18:10Z-
dc.date.available2016-10-25T18:14:15Z-
dc.date.issued1997-01-01-
dc.identifierhttp://dx.doi.org/10.1109/ACC.1997.609492-
dc.identifier.citationProceedings of the American Control Conference, v. 6, p. 3592-3596.-
dc.identifier.issn0743-1619-
dc.identifier.urihttp://hdl.handle.net/11449/64990-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/64990-
dc.description.abstractAnalog networks for solving convex nonlinear unconstrained programming problems without using gradient information of the objective function are proposed. The one-dimensional net can be used as a building block in multi-dimensional networks for optimizing objective functions of several variables.en
dc.format.extent3592-3596-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectNonlinear programming-
dc.subjectObject oriented programming-
dc.subjectProblem solving-
dc.subjectAnalog nonderivative optimizers-
dc.subjectOptimization-
dc.titleAnalog nonderivative optimizersen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationFEIS/UNESP, Ilha Solteira-
dc.description.affiliationUnespFEIS/UNESP, Ilha Solteira-
dc.identifier.doi10.1109/ACC.1997.609492-
dc.identifier.wosWOS:A1997BJ29B00769-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofProceedings of the American Control Conference-
dc.identifier.scopus2-s2.0-0030686202-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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