Nonlinear signal separation for multinonlinearity constrained mixing model

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)796-802
Number of pages7
JournalIEEE Transactions on Neural Networks
Volume17
Issue number3
DOIs
Publication statusPublished - 8 May 2006

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