Blind source separation using order statistics
Signal Processing
Journal of VLSI Signal Processing Systems
Neural-Based Separating Method for Nonlinear Mixtures
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
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Post-nonlinear blind source separation using wavelet neural networks and particle swarm optimization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Research of blind deconvolution algorithm based on high-order statistics and quantum inspired GA
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Convergence analysis of a GA-ICA algorithm
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
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This paper presents a novel method for blindly separating unobservable independent source signals from their nonlinear mixtures. The demixing system is modeled using a parameterized neural network whose parameters can be determined under the criterion of independence of its outputs. Two cost functions based on higher order statistics are established to measure the statistical dependence of the outputs of the demixing system. The proposed method utilizes a genetic algorithm (GA) to minimize the highly nonlinear and nonconvex cost functions. The GA-based global optimization technique is able to obtain superior separation solutions to the nonlinear blind separation problem from any random initial values. Compared to conventional gradient-based approaches, the GA-based approach for blind source separation is characterized by high accuracy, robustness, and convergence rate. In particular, it is very suitable for the case of limited available data. Simulation results are discussed to demonstrate that the proposed GA-based approach is capable of separating independent sources from their nonlinear mixtures generated by a parametric separation model