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Robust nonlinear signal separation using regularised maximum likelihood neural network

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

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Abstract

The fundamental problem in independent component analysis (ICA) is to find a set of statistically independent components from the output of a mixing system. Almost all of the existing algorithms are based on the ideal situation where the mixture is a linear. However, in some practical situations, the signals are nonlinearly mixed and thus the problem results in ill-posed solution. A robust nonlinear technique is presented for instantaneous signal separation of nonlinear mixtures based on regularised maximum likelihood estimation combined with multiple-layer neural network. The motivation for such criterion is to incorporate a priori information such as smoothness constraints into the statement of the ill-posed problem so that convergence to undesirable minima can be avoided by the neural network. (8 References).


Publication metadata

Author(s): Woo WL, Dlay SS

Publication type: Article

Publication status: Published

Journal: WSEAS Transactions on Systems

Year: 2003

Volume: 2

Issue: 3

Pages: 675-680

Print publication date: 01/01/2003

ISSN (print): 1109-2777

Publisher: World Scientific and Engineering Academy and Society


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