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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1046

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contributor.authorSubudhi, B-
contributor.authorJena, D-
date.accessioned2009-09-14T15:48:14Z-
date.available2009-09-14T15:48:14Z-
date.issued2009-
identifier.citationInternational Journal of Automation and Computing, Volume 6, No 2, May 2009, Pages 137-144en
identifier.urihttp://dx.doi.org/10.1007/s11633-009-0137-0-
identifier.urihttp://hdl.handle.net/2080/1046-
description.abstractThis paper presents an improved nonlinear system identi¯cation scheme using di®erential evolution (DE), neural network (NN) and Levenberg Marquardt algorithm (LM). With a view to achieve better convergence of NN weights optimization during the training, the DE and LM are used in a combined framework to train the NN. We present the convergence analysis of the DE and demonstrate the e±cacy of the proposed improved system identi¯cation algorithm by exploiting the combined DE and LM training of the NN and suitably implementing it together with other system identi¯cation methods, namely NN and DE+NN on a number of examples including a practical case study. The identi¯cation results obtained through a series of simulation studies of these methods on di®erent nonlinear systems demonstrate that the proposed DE and LM trained NN approach to nonlinear system identi¯cation can yield better identi¯cation results in terms of time of convergence and less identi¯cation error.en
format.extent3303712 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherSpringeren
subjectDifferential evolutionen
subjectneural network (NN)en
subjectnonlinear system identificationen
subjectLevenberg Marquardt algorithmen
titleAn Improved Differential Evolution Trained Neural Network Scheme for Nonlinear System Identificationen
typeArticleen
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