Browse by author
Lookup NU author(s): Dr Wai Lok Woo,
Professor Satnam Dlay
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
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).
Author(s): Woo WL, Dlay SS
Publication type: Article
Publication status: Published
Journal: WSEAS Transactions on Systems
Print publication date: 01/01/2003
ISSN (print): 1109-2777
Publisher: World Scientific and Engineering Academy and Society