Post-nonlinear blind source separation using wavelet neural networks and particle swarm optimization

Authors:
Ying Gao;Shengli Xie
Affiliations:
Dept. of Computer Science and Technology, Guangzhou University, Guangzhou, China;College of Electronic & Information Engineering, South China University of Technology, Guangzhou, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

Blind source separation of post-nonlinear mixtures is discussed. The demixing system of the post-nonlinear mixtures is modeled using a multi-input multi-output wavelet neural network whose parameters can be determined under the criterion of independence of its outputs. A criterion of independence based on higher order moments is used to measure the statistical dependence of the outputs of the demixing system, and the particle swarm optimization technique is utilized to minimized the criterion. Simulation results show that the proposed approach is capable of separating independent sources from their post-nonlinear mixtures.