dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T13:27:12Z
dc.date.available2014-05-20T13:27:12Z
dc.date.created2014-05-20T13:27:12Z
dc.date.issued2007-01-01
dc.identifierApplied Mathematical Modelling. New York: Elsevier B.V., v. 31, n. 1, p. 78-92, 2007.
dc.identifier0307-904X
dc.identifierhttp://hdl.handle.net/11449/8885
dc.identifier10.1016/j.apm.2005.08.007
dc.identifierWOS:000242415200006
dc.identifierWOS000242415200006.pdf
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.
dc.languageeng
dc.publisherElsevier B.V.
dc.relationApplied Mathematical Modelling
dc.relation2.617
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjectnonlinear optimization problems
dc.subjectrecurrent neural networks
dc.subjectHopfield networks
dc.subjectnonlinear programming
dc.titleA novel approach based on recurrent neural networks applied to nonlinear systems optimization
dc.typeArtículos de revistas


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