Analog nonderivative optimizers

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorTeixeira, Marcelo C M
dc.creatorZak, Stanislaw H.
dc.date2014-05-27T11:18:10Z
dc.date2016-10-25T18:14:15Z
dc.date2014-05-27T11:18:10Z
dc.date2016-10-25T18:14:15Z
dc.date1997-01-01
dc.date.accessioned2017-04-06T00:48:48Z
dc.date.available2017-04-06T00:48:48Z
dc.identifierProceedings of the American Control Conference, v. 6, p. 3592-3596.
dc.identifier0743-1619
dc.identifierhttp://hdl.handle.net/11449/64990
dc.identifierhttp://acervodigital.unesp.br/handle/11449/64990
dc.identifier10.1109/ACC.1997.609492
dc.identifierWOS:A1997BJ29B00769
dc.identifier2-s2.0-0030686202
dc.identifierhttp://dx.doi.org/10.1109/ACC.1997.609492
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/886758
dc.descriptionAnalog 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.
dc.languageeng
dc.relationProceedings of the American Control Conference
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectNonlinear programming
dc.subjectObject oriented programming
dc.subjectProblem solving
dc.subjectAnalog nonderivative optimizers
dc.subjectOptimization
dc.titleAnalog nonderivative optimizers
dc.typeOtro


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