Novel nonlinear signals separation of optimized entropy based on adaptive natural gradient learning

Authors:
Ren Ren;Jin Xu;Shihua Zhu;Danan Ren;Yongqiang Luo
Affiliations:
School of Electronic & Information Engineering, Xian Jiao Tong University, Xi’an, P.R. China;Institute of Biomedical Engineering, Xian Jiao Tong University, Xi’an, P.R. China;School of Electronic & Information Engineering, Xian Jiao Tong University, Xi’an, P.R. China;Department of Mathematics, Northwest University, Xi’an, P.R. China;School of Electronic & Information Engineering, Xian Jiao Tong University, Xi’an, P.R. China
Venue:
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Year:
2005

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Abstract

Without knowing the signal probability distribution and channel, novel blind source separation (BSS) of singular value decomposition (SVD) with adaptive minimizing mutual information is proposed to extract mixed signals. Adaptive natural gradient decent algorithm attains fast convergence speed and reliability. We focus on applying cost function BSS and SVD to achieve the solution of decomposition signals. The results indicate that the SVD combining minimizing mutual information can predict the extent of mixed signal and searching direction. The simulation illustrates that the method improves the performance, convergence and reliability. The different results can be attained by distinctive nonlinear function. The algorithm of adaptive changing de-mixed function is a better way to break through the limitation of nonlinear BSS.