Post-nonlinear Blind Source Separation Using Wavelet Neural Networks and Particle Swarm Optimization

• Published in: Advances in Natural Computation pp. 386 - 390
• Main Authors: Gao, Ying, Xie, Shengli
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Exact sciences and technology; Software; MIMO system; Source separation; Statistical analysis; Probabilistic approach; Wavelet transformation; Non linear model; Particle system; Mixture theory; Swarm intelligence; Neural network; Blind separation
• ISBN: 9783540283256; 3540283250; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539117_56

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.