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dc.contributor.authorSatapathy, J K-
dc.contributor.authorDas, Susmita-
dc.identifier.citationInternational Conference on Signal Processing and Communications, SPCOM '04 11-14 Dec, P 472-475en
dc.descriptionPersonal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEEen
dc.description.abstractAdaptive equalisers are characterised in general by their structures, the learning algorithms and the use of training sequences. This paper presents a novel technique of improving the performance of conventional multilayer perceptron(MLP) based decision feedback equaliser (DFE) of reduced structural complexity by adapting the slope of the sigmoidal activation function using fuzzy logic control technique. The adaptation of the slope parameter increases the degrees of freedom in the weight space of the conventional Feedforward Neural Network (CFNN) configuration. Application of this technique reduces the structural complexity of a conventional FNN equaliser, provides faster learning and significant performance gain.en
dc.format.extent564197 bytes-
dc.subjectmultilayer perceptronen
dc.subjectdecision feedback equaliseren
dc.subjectconventional Feedforward Neural Networken
dc.titleBER performance improvement of an FNN based equaliser using fuzzy tuned sigmoidal activation functionen
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