ISSN : 1796-203X
Volume : 2    Issue : 6    Date : August 2007

Extraction of Unique Independent Components for Nonlinear Mixture of Sources
Pei Gao, Li Chin Khor, Wai Lok Woo and Satnam Singh Dlay
Page(s): 9-16
Full Text:
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In this paper, a neural network solution to extract independent components from nonlinearly mixed
signals is proposed. Firstly, a structurally constrained mixing model is introduced to extend the
recently proposed mono-nonlinearity mixing model, allowing that different nonlinear distortion are
applied to each source signal. Based on this nonlinear mixing model, a novel demixing system
characterized by polynomial neural network is then proposed for recovering the original sources.
The parameter learning algorithm is derived mathematically based on the minimum mutual
information principle. It is shown that unique extraction of independent components can be
achieved by optimizing the mutual information cost function under both model structure and signal
constraints. In this framework, the theory of series reversion is developed with the aim to perform
dual optimization on the polynomials of the proposed demixing system. Finally, simulation results
are presented to verify the efficacy of the proposed approach.

Index Terms
Nonlinear independent component analysis, Nonlinear blind source separation, polynomial neural
network, unsupervised learning