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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/223

Title: BER performance improvement of an FNN based equaliser using fuzzy tuned sigmoidal activation function
Authors: Satapathy, J K
Das, Susmita
Keywords: multilayer perceptron
decision feedback equaliser
conventional Feedforward Neural Network
Issue Date: 2004
Citation: International Conference on Signal Processing and Communications, SPCOM '04 11-14 Dec, P 472-475
Abstract: Adaptive 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.
Description: Personal 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 IEEE
URI: http://hdl.handle.net/2080/223
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