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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/684

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contributor.authorSubudhi, B-
contributor.authorJena, D-
date.accessioned2008-05-13T08:46:26Z-
date.available2008-05-13T08:46:26Z-
date.issued2008-
identifier.citationNeural Processing Letters, Vol 27, Iss 3, P 285-296en
identifier.urihttp//dx.doi.org/10.1007/s11063-008-9077-x-
identifier.urihttp://hdl.handle.net/2080/684-
descriptionCopyright for the published version belongs to Springeren
description.abstractThis paper proposes a new nonlinear system identification scheme using differential evolution (DE), neural network and Levenberg Marquardt algorithm (LM). Here, DE and LM in a combined framework are used to train a neural network for achieving better convergence of neural network weight optimization. A number of examples including a practical case-study have been considered for implementation of different system identification methods namely, only NN, DE+NN and DE+LM+NN. After, a series of simulation studies of these methods on the different nonlinear systems it has been confirmed that the proposed DE and LM trained NN approach to nonlinear system identification has yielded better identification results in terms of time of convergence and less identification error. © 2008 Springer Science+Business Media, LLC.en
format.extent933681 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherSpringeren
subjectDifferential evolutionen
subjectEvolutionary computationen
subjectLevenberg Marquardten
subjectNonlinear system identificationen
titleDifferential evolution and levenberg marquardt trained neural network scheme for nonlinear system identificationen
typeArticleen
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