Advertisement

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)

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhang, L., Amari, S.: Estimating function approach to multi-channel blind deconvolution. In: Circuits and Systems IEEE APCCAS 2000, vol. 12(4-6), pp. 587–590 (2000)Google Scholar
  2. 2.
    Amari, S.: Superefficiency in blind source separation. IEEE trans. on signal processing 47(4), 936–944 (1999)CrossRefGoogle Scholar
  3. 3.
    Tsatsanis, M.K., Giannakis, G.B.: Blind estimation of direct sequence spread spectrum signals in multipath. IEEE Trans. Signal Processing 45(5), 1241–1252 (1997)CrossRefGoogle Scholar
  4. 4.
    Tugnait, J.K.: Blind spatio-temporal equalization and impulse response estimation for MIMO channels using a Godard cost function. IEEE Trans. Signal Processing 45(1), 268–271 (1997)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Tugnait, J.K.: Adaptive blind separation of convolutive mixtures of independent linear signals. EURASIP Journal Signal Processing 73(1-2), 139–152 (1999)MATHGoogle Scholar
  6. 6.
    Cruces-Alvarez, S.A., Cichocki, A., Amari, A.: From blind signal extraction to blind instantaneous signal separation: criteria, algorithms, and stability. IEEE Trans. on Neural Networks 15(4), 859–873 (2004)CrossRefGoogle Scholar
  7. 7.
    Belouchrani, A., Abed-Meraim, K.: Blind separation of nonstationary sources. IEEE Signal Processing Letters 11(7), 605–608 (2004)CrossRefGoogle Scholar
  8. 8.
    Ferreol, A., Chevalier, P.: Second-order blind separation of first- and second-order cyclostationary sources-application to AM, FSK, CPFSK, and deterministic sources. IEEE Trans. on Signal Processing 52(4), 845–861 (2004)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Asano, F., Ikeda, S., Ogawa, M., Asoh, H.: Combined approach of array processing and independent component analysis for blind separation of acoustic signals. IEEE Trans. Speech and Audio Processing 11(3), 204–210 (2003)CrossRefGoogle Scholar
  10. 10.
    Cardoso, J.-F., Souloumiac, A.: Blind beamforming for non Gaussian signals. IEE-Proceedings-F 140(6), 362–370 (1993)Google Scholar
  11. 11.
    Jutten, C., Herault, J.: Separation of sources. Part i. Signal Processing 24(1), 1–10 (1991)MATHCrossRefGoogle Scholar
  12. 12.
    Bell, A.J., Sejnowski, T.J.: An information maximization approach to blind separation and blind deconvolution. Neural Computation 7(6), 1129–1159 (1995)CrossRefGoogle Scholar
  13. 13.
    Comon, P.: Independent component analysis, a new concept. Signal Processing 36(3), 287–314 (1994)MATHCrossRefGoogle Scholar
  14. 14.
    Amari, S., Cichocki, A., Yang, H.H.: A new learning algorithm for blind signal separation. Advances in Neural Information Processing Systems 8, 757–763 (1996)Google Scholar

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

Personalised recommendations