Nonlinear blind source separation using higher order statistics and a genetic algorithm

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.

[1]  Ali Mansour,et al.  Blind Separation of Sources , 1999 .

[2]  J. Miller.  Digital filter design at gate-level using evolutionary algorithms , 1999 .

[3]  Aapo Hyvärinen,et al.  Nonlinear independent component analysis: Existence and uniqueness results , 1999, Neural Networks.

[4]  Jean-Francois Cardoso,et al.  Blind signal separation: statistical principles , 1998, Proc. IEEE.

[5]  Shun-ichi Amari,et al.  Adaptive blind signal processing-neural network approaches , 1998, Proc. IEEE.

[6]  Holger Broman,et al.  Source separation using a criterion based on second-order statistics , 1998, IEEE Trans. Signal Process..

[7]  Y. Wu,et al.  Genetic algorithm optimisation for maximum likelihood joint channel and data estimation , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[8]  Christian Jutten,et al.  Source separation in post nonlinear mixtures: an entropy-based algorithm , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[9]  S. Omatu,et al.  Signal separation method using genetic algorithm , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[10]  Majeed Alkanhal,et al.  Blind identification of nonminimum phase FIR systems: Cumulants matching via genetic algorithms , 1998, Signal Process..

[11]  Sheng Chen,et al.  Genetic algorithm optimization for blind channel identification with higher order cumulant fitting , 1997, IEEE Trans. Evol. Comput..

[12]  H. H. Yang,et al.  Information backpropagation for blind separation of sources in nonlinear mixture , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[13]  C. Papadias,et al.  A constant modulus algorithm for multiuser signal separation in presence of delay spread using antenna arrays , 1997, IEEE Signal Processing Letters.

[14]  Gustavo Deco,et al.  Nonlinear higher-order statistical decorrelation by volume-conserving neural architectures , 1995, Neural Networks.

[15]  Peter J. Fleming,et al.  Parallel Genetic Algorithms: A Survey , 1994 .

[16]  William A. Sethares,et al.  Nonlinear parameter estimation via the genetic algorithm , 1994, IEEE Trans. Signal Process..

[17]  G. Burel.  Original Contribution: Blind separation of sources: A nonlinear neural algorithm , 1992 .

[18]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[19]  Kalmanje Krishnakumar,et al.  Micro-Genetic Algorithms For Stationary And Non-Stationary Function Optimization , 1990, Other Conferences.

[20]  Christian Jutten,et al.  Space or time adaptive signal processing by neural network models , 1987 .

[21]  J. Zurada,et al.  Nonlinear blind source separation using a radial basis function network , 2001, IEEE Trans. Neural Networks.

[22]  Petteri Pajunen,et al.  Nonlinear Blind Source Separation by Self-Organizing Maps , 1996 .

[23]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[24]  Terrence J. Sejnowski,et al.  Adaptive separation of mixed broadband sound sources with delays by a beamforming Herault-Jutten network , 1995 .

[25]  Signal Processing , 1991 .

[26]  Stan Matwin,et al.  Genetic algorithms approach to a negotiation support system , 1991, IEEE Trans. Syst. Man Cybern..

[27]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[28]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[29]  S. Talwar,et al.  A subspace approach to blind space-time signal processing for wireless communication systems , 1997, IEEE Trans. Signal Process..