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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1162

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contributor.authorGuha, D R-
contributor.authorPatra, S K-
date.accessioned2010-01-30T12:53:40Z-
date.available2010-01-30T12:53:40Z-
date.issued2009-
identifier.citation5th International Conference on MEMS NANO, and Smart Systems (ICMENS 2009), Dubai, December 28-30,2009en
identifier.urihttp://hdl.handle.net/2080/1162-
description.abstractThis paper presents a novel technique in channel equalization. Wireless communication system is affected by inter-symbol interference, co-channel interference and Burst noise interference in the presence of additive white Gaussian noise. Different equalization techniques have been used to mitigate these effects using Artificial Neural Networks based Multilayer Perceptron Network, Radial Basis Function, Recurrent Network, Fuzzy and Adaptive Neuro fuzzy System, and also using linear adaptive LMS, RLS system. In this paper we proposed a RBF based equalizer which is trained using wilcoxon learning method. The equalizer presented shows considerable performance gain. Simulation studies have been conducted to demonstrate the performance of wilcoxon training for this class of problem.en
format.extent286829 bytes-
format.mimetypeapplication/pdf-
language.isoen-
subjectChannel Equalizer,en
subjectArtificial neural networks,en
subjectMultilayer perceptron network,en
subjectRadial basis function,en
subjectwilcoxon generalized radial basis function network,en
subjectLinear channel,en
subjectBack Propagation,en
subjectLeast mean Square,en
subjectRecursive least squares.en
titleISI & Burst Noise Interference Minimization using Wilcoxon Generalized Radial basic Function Equalizeren
typeArticleen
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