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dc.contributor.authorPatra, S K-
dc.contributor.authorMulgrew, B-
dc.identifier.citationProceedings of 1997 IEEE International Symposium on : Circuits and Systems, 9-12 June 1997, Hong Kongen
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 IEEE.en
dc.description.abstractAbstract This paper investigates the computational aspects of radial basis function (RBF) equalizers. In an RBF implementation of the Bayesian equalizer the RBF centers are placed at equalizer channel states and the output layer weights are adjusted to +1/-1. Here we propose an RBF equalizer with scalar centers which can implement the Bayesian decision function. The proposed RBF equalizer provides lower computational complexity compared to the reported RBF equalizers and can efficiently employ subset center selection for computing the decision function resulting in a substantial reduction in computational complexity.en
dc.format.extent422938 bytes-
dc.subjectISCAS '97en
dc.subjectBayes methodsen
dc.subjectadaptive equalisersen
dc.subjectcomputational complexityen
dc.titleComputational aspects of adaptive radial basis function equalizer designen
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