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dc.contributor.authorda Silva, Ivan Nunes-
dc.contributor.authordo Amaral, Wagner Caradori-
dc.contributor.authorde Arruda, Lucia Valeria-
dc.date.accessioned2014-05-20T13:27:12Z-
dc.date.available2014-05-20T13:27:12Z-
dc.date.issued2007-01-01-
dc.identifierhttp://dx.doi.org/10.1016/j.apm.2005.08.007-
dc.identifier.citationApplied Mathematical Modelling. New York: Elsevier B.V., v. 31, n. 1, p. 78-92, 2007.-
dc.identifier.issn0307-904X-
dc.identifier.urihttp://hdl.handle.net/11449/8885-
dc.description.abstractThis paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network. (c) 2005 Elsevier B.V. All rights reserved.en
dc.format.extent78-92-
dc.language.isoeng-
dc.publisherElsevier B.V.-
dc.sourceWeb of Science-
dc.subjectnonlinear optimization problemspt
dc.subjectrecurrent neural networkspt
dc.subjectHopfield networkspt
dc.subjectnonlinear programmingpt
dc.titleA novel approach based on recurrent neural networks applied to nonlinear systems optimizationen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationSão Paulo State Univ, Dept Elect Engn, UNESP, BR-17033360 Bauru, SP, Brazil-
dc.description.affiliationUnespSão Paulo State Univ, Dept Elect Engn, UNESP, BR-17033360 Bauru, SP, Brazil-
dc.identifier.doi10.1016/j.apm.2005.08.007-
dc.identifier.wosWOS:000242415200006-
dc.rights.accessRightsAcesso aberto-
dc.identifier.fileWOS000242415200006.pdf-
dc.relation.ispartofApplied Mathematical Modelling-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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