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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/397

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contributor.authorDash, P K-
contributor.authorSamantaray, S R-
date.accessioned2007-01-08T10:45:00Z-
date.available2007-01-08T10:45:00Z-
date.issued2004-
identifier.citationEngineering Intelligent Systems, Vol 4, P 205-210en
identifier.urihttp://hdl.handle.net/2080/397-
descriptionCopyright for this article belongs to the publisheren
description.abstractThe paper presents a new fault classification scheme for high speed relaying using minimal radial basis function neural network. Unlike earlier approaches in using radial basis function network, the new approach reduces the training time drastically and provides a systematic framework for selecting the number of neurons in the hidden layer. Further the minimal radial basis function network yields an accurate fault type classification on a transmission line even in the presence of high fault resistance in the fault path. The paper also presents two different approaches in generating the inputs to the neural network with a view to simplify the training procedure and reduce the complexity in calculations. Several computer simulated test results are presented to highlight the effectiveness of the new approach.en
format.extent358314 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherCRC Publicationsen
subjectRBF neural networken
subjectFault Classificationen
subjectNetwork Input Generationen
titleAn accurate fault classification algorithm using a minimal radial basis function neural networken
typeArticleen
Appears in Collections:Journal Articles

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