Blind Source Separation in Post-nonlinear Mixtures Using Natural Gradient Descent and Particle Swarm Optimization Algorithm

Authors:
Kai Song;Mingli Ding;Qi Wang;Wenhui Liu
Affiliations:
Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, 150001, China;Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, 150001, China;Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, 150001, China;Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, 150001, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

Extracting independent source signals from their nonlinear mixtures is a very important issue in many realistic models. This paper proposes a new method for solving nonlinear blind source separation (NBSS) problems by exploiting particle swarm optimization (PSO) algorithm and natural gradient descent. First, we address the problem of separation of mutually independent sources in post-nonlinear mixtures. The natural gradient descent is used to estimate the separation matrix. Then we define the mutual information between output signals as the fitness function of PSO. The mutual information is used to measure the statistical dependence of the outputs of the demixing system. PSO can rapidly obtain the globally optimal coefficients of the higher order polynomial functions. Compared to conventional NBSS approaches, the main characteristics of this method are its simplicity, the rapid convergence and high accuracy. In particular, it is robust against local minima in search for inverse functions. Experiments are discussed to demonstrate these results.