Nonlinear blind source separation using hybrid neural networks

Authors:
Chun-Hou Zheng;Zhi-Kai Huang;Michael R. Lyu;Tat-Ming Lok
Affiliations:
Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China;Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China;Computer Science & Engineering Dept., The Chinese University of Hong Kong, Hong Kong;Information Engineering Dept., The Chinese University of Hong Kong, Shatin, Hong Kong
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

This paper proposes a novel algorithm based on minimizing mutual information for a special case of nonlinear blind source separation: post-nonlinear blind source separation. A network composed of a set of radial basis function (RBF) networks, a set of multilayer perceptron and a linear network is used as a demixing system to separate sources in post-nonlinear mixtures. The experimental results show that our proposed method is effective, and they also show that the local character of the RBF network’s units allows a significant speedup in the training of the system.