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Nonlinear signal separation for multinonlinearity constrained mixing model

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

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Abstract

In this letter, a new type of nonlinear mixture is derived and developed into a multinonlinearity constrained mixing model. The proposed signal separation solution integrates the Theory of Series Reversion with a polynomial neural network whereby the hidden neurons are spanned by a set of mutually reversed activation functions. Simulations have been undertaken to support the theory of the proposed scheme and the results indicate promising performance. © 2006 IEEE.


Publication metadata

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

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Neural Networks

Year: 2006

Volume: 17

Issue: 3

Pages: 796-802

ISSN (print): 1045-9227

ISSN (electronic): 1941-0093

Publisher: IEEE

URL: http://dx.doi.org/10.1109/TNN.2006.873288

DOI: 10.1109/TNN.2006.873288

PubMed id: 16722182


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