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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/828

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contributor.authorSubudhi, B-
contributor.authorJena, Debashisha-
contributor.authorGupta, Madan M-
date.accessioned2009-05-18T06:02:08Z-
date.available2009-05-18T06:02:08Z-
date.issued2008-
identifier.citationIEEE Region 10 Colloquium and 3rd International Conference on Industrial and Information Systems, ICIIS, Kharagpur, India December 8-10, 2008en
identifier.urihttp://hdl.handle.net/2080/828-
description.abstractSeveral gradient-based approaches such as back-propagation, conjugate gradient and Levenberg Marquardt (LM) methods have been developed for training the neural network (NN) based systems. Still, in some situations, like multimodal cost function, these procedures may lead to some local minima, therefore, the evolutionary algorithms (EAs) based procedures were considered as a promising alternative. In this paper we focus on a memetic algorithm based approach for training the neural networks. We use the memetic algorithm (MA) for training the multilayer perceptrons as applied to nonlinear system identification. The proposed memetic algorithm is an alternative to gradient search methods, such as back-propagation, which have inherent limitations of many local optima. Here we have studied the identification of a nonlinear system using five different algorithms namely Back-propagation (BP), Genetic Algorithm (GA), Differential Evolution (DE), Genetic Algorithm Back-propagation (GABP), along with the proposed Differential Evolution plus Back-propagation (DEBP) approaches. In the proposed system identification scheme, we have exploited two global search methods namely genetic algorithm (GA), and differential evolution (DE). These two global search methods have been hybridized with the gradient descent method i.e. the back propagation (BP) algorithm. The local search BP algorithm is used as an operator for genetic algorithm and differential evolution. These algorithms have been tested on a standard benchmark problem for nonlinear system identification to prove their efficacy.en
format.extent304751 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIEEEen
subjectBack propagationen
subjectDifferential evolutionen
subjectEvolutionary computationen
subjectNonlinear system identificationen
titleMemetic Differential Evolution Trained Neural Networks for Nonlinear System Identificationen
typeArticleen
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