Analog nonderivative optimizers

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:18:10Z
dc.date.available2014-05-27T11:18:10Z
dc.date.created2014-05-27T11:18:10Z
dc.date.issued1997-01-01
dc.identifierProceedings of the American Control Conference, v. 6, p. 3592-3596.
dc.identifier0743-1619
dc.identifierhttp://hdl.handle.net/11449/64990
dc.identifier10.1109/ACC.1997.609492
dc.identifierWOS:A1997BJ29B00769
dc.identifier2-s2.0-0030686202
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.
dc.languageeng
dc.relationProceedings of the American Control Conference
dc.relation0,500
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectNonlinear programming
dc.subjectObject oriented programming
dc.subjectProblem solving
dc.subjectAnalog nonderivative optimizers
dc.subjectOptimization
dc.titleAnalog nonderivative optimizers
dc.typeActas de congresos


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