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Neural network approaches to nonlinear blind source separation

Lookup NU author(s): Pei Gao, Dr Wai Lok Woo, Professor Satnam Dlay


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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. © 2005 IEEE.

Publication metadata

Author(s): Gao P, Woo WL, Dlay SS

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 8th International Symposium on Signal Processing and its Applications (ISSPA)

Year of Conference: 2005

Pages: 78-81

Publisher: IEEE


DOI: 10.1109/ISSPA.2005.1580200

Library holdings: Search Newcastle University Library for this item

ISBN: 0780392434