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dc.contributor.authorda Silva, I. N.
dc.contributor.authorNepomuceno, L.
dc.date.accessioned2014-05-20T13:27:14Z
dc.date.available2014-05-20T13:27:14Z
dc.date.issued2001-01-01
dc.identifierhttp://dx.doi.org/10.1109/PESS.2001.970255
dc.identifier.citation2001 Power Engineering Society Summer Meeting, Vols 1-3, Conference Proceedings. New York: IEEE, p. 1269-1274, 2001.
dc.identifier.urihttp://hdl.handle.net/11449/8906
dc.description.abstractA neural approach to solve the problem defined by the economic load dispatch in power systems is presented in this paper, Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements the ability of neural networks to realize some complex nonlinear function makes them attractive for system optimization the neural networks applyed in economic load dispatch reported in literature sometimes fail to converge towards feasible equilibrium points the internal parameters of the modified Hopfield network developed here are computed using the valid-subspace technique These parameters guarantee the network convergence to feasible quilibrium points, A solution for the economic load dispatch problem corresponds to an equilibrium point of the network. Simulation results and comparative analysis in relation to other neural approaches are presented to illustrate efficiency of the proposed approach.en
dc.format.extent1269-1274
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartof2001 Power Engineering Society Summer Meeting, Vols 1-3, Conference Proceedings
dc.sourceWeb of Science
dc.subjecteconomic dispatchpt
dc.subjectartificial neural networkspt
dc.subjectHopfield modelpt
dc.subjectnonlinear optimizationpt
dc.titleAn efficient neural approach to economic load dispatch in power systemsen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIEEE
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationUNESP, Dept Elect Engn, BR-17033360 Bauru, SP, Brazil
dc.description.affiliationUnespUNESP, Dept Elect Engn, BR-17033360 Bauru, SP, Brazil
dc.identifier.doi10.1109/PESS.2001.970255
dc.identifier.wosWOS:000176406700275
dc.rights.accessRightsAcesso aberto
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Engenharia, Baurupt
dc.identifier.lattes2013445187247691
unesp.author.lattes2013445187247691
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