Neural network approaches to nonlinear blind source separation

P. Gao*, W. L. Woo, S. S. Dlay

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In this paper, several recently proposed neural network approaches to nonlinear blind signal separation (BSS) are reviewed. Of great interest, popular multilayer perceptron (MLP), radial basis function (RBF) and polynomial neural networks are the focus of the paper. In order to uniquely extract the original source signals from only nonlinearly mixed observations, some forms of constrains are always imposed on the neural networks. Three structurally constrained nonlinear independent component analysis mixing models are presented, followed by the discussion on additional signal constraints to the original cost function stemmed from the Kullback-Leibler Divergence.

Original languageEnglish
Title of host publicationProceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005
PublisherIEEE
Pages78-81
Number of pages4
Volume1
ISBN (Print)0780392434
DOIs
Publication statusPublished - 30 Jan 2006
Event8th International Symposium on Signal Processing and its Applications, ISSPA 2005 - Sydney, Australia
Duration: 28 Aug 200531 Aug 2005

Conference

Conference8th International Symposium on Signal Processing and its Applications, ISSPA 2005
CountryAustralia
CitySydney
Period28/08/0531/08/05

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