A neural system to robust Nonlinear optimization subject to disjoint and constrained sets

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dc.contributor.authorda Silva, I. N.
dc.contributor.authorde Souza, A. N.
dc.contributor.authorBordon, M. E.
dc.contributor.authorUlson, Jose Alfredo Covolan [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T13:27:14Z
dc.date.available2014-05-20T13:27:14Z
dc.date.issued2001-01-01
dc.description.abstractThe ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter estimation problems for nonlinear models with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.en
dc.description.affiliationState Univ São Paulo, UNESP, Dept Elect Engn, BR-17033360 Bauru, SP, Brazil
dc.description.affiliationUnespState Univ São Paulo, UNESP, Dept Elect Engn, BR-17033360 Bauru, SP, Brazil
dc.format.extent7-12
dc.identifierhttp://dl.acm.org/citation.cfm?id=704386
dc.identifier.citationWorld Multiconference on Systemics, Cybernetics and Informatics, Vol 1, Proceedings. Orlando: Int Inst Informatics & Systemics, p. 7-12, 2001.
dc.identifier.lattes8212775960494686
dc.identifier.lattes5589838844298232
dc.identifier.lattes4517057121462258
dc.identifier.orcid0000-0001-8510-8245
dc.identifier.urihttp://hdl.handle.net/11449/8905
dc.identifier.wosWOS:000175785900002
dc.language.isoeng
dc.publisherInt Inst Informatics & Systemics
dc.relation.ispartofWorld Multiconference on Systemics, Cybernetics and Informatics, Vol 1, Proceedings
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectneural networkspt
dc.subjectrobust estimationpt
dc.subjectparameter identificationpt
dc.subjectestimation algorithmspt
dc.titleA neural system to robust Nonlinear optimization subject to disjoint and constrained setsen
dc.typeTrabalho apresentado em evento
dcterms.rightsHolderInt Inst Informatics & Systemics
unesp.author.lattes8212775960494686[2]
unesp.author.lattes5589838844298232
unesp.author.lattes4517057121462258
unesp.author.orcid0000-0002-8617-5404[2]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Engenharia, Baurupt

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Artigos - Engenharia Elétrica - FEB