Novel Nonlinear Signals Separation of Optimized Entropy Based on Adaptive Natural Gradient Learning

  • Ren Ren
  • Jin Xu
  • Shihua Zhu
  • Danan Ren
  • Yongqiang Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


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.


Mutual Information Singular Value Decomposition Independent Component Analysis Independent Component Analysis Blind Source Separation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ren Ren
    • 1
    • 2
  • Jin Xu
    • 3
  • Shihua Zhu
    • 1
  • Danan Ren
    • 4
  • Yongqiang Luo
    • 1
  1. 1.School of Electronic & Information EngineeringXian Jiao Tong UniversityXi’anP.R. China
  2. 2.Department of PhysicsXian Jiao Tong UniversityXi’anP.R. China
  3. 3.Institute of Biomedical EngineeringXian Jiao Tong UniversityXi’anP.R. China
  4. 4.Department of MathematicsNorthwest UniversityXi’anP.R. China

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