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A New Step-Adaptive Natural Gradient Algorithm for Blind Source Separation

  • Huan Tao
  • Jian-yun Zhang
  • Lin Yu
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 344)

Abstract

The main differences between the mixed signals and origin signals: Gaussian probability density function, statistical independence and temporal predictability. The proposed BSS algorithms mainly derived from the Gaussian probability density function and statistical independence. A new adaptive method is proposed in the paper. The method uses the temporal predictability as cost function which is not studied as much as other generic differences between the properties of signals and their mixtures. Step-adaptive nature gradient algorithm is proposed to separate signals, which is more robust and effective. Compared to fixed step natural gradient algorithm, Simulations show a good performance of the algorithm.

Keywords

Gradient Algorithm Blind Source Separation Statistical Independence Correlation Coefficiency Fixed Step 
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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References

  1. 1.
    James, V. Stone.: Blind Source Separation Using Temporal Predictability, Neural computation (in press) (2001) 1196–1199Google Scholar
  2. 2.
    Belouchrani, A., Abed-Meraim, K., Cardoso, J.F.: A Blind Source Separation Using Second Order Statistics. IEEE Trans. On signal processing, Feb. Vol.45 (1997)434–444CrossRefGoogle Scholar
  3. 3.
    Amari, S I.:Natural Gradient Works Efficiently in Learning. Neural Computation(1998) 251–276Google Scholar
  4. 4.
    Amari, S. I., Chen, T. P., Cichocki, A.: Stability Analysis of Adaptive Blind Source Separation, Neural Networks(1997) 1345–1351.Google Scholar
  5. 5.
    Sergio, A., Cruces-Alvarez, Andrzej Cichocki, Shun-Ichi Amari.: On A New Blind Signal Extraction Algorithm: Different Criteria and Stability Analysis., IEEE SIGNAL PROCESSING LETTERS, VOL.9, NO.8, AUGUST (2002)Google Scholar
  6. 6.
    Yan, Li, Peng Wen, David Powers.: Methods for The Blind Signal Separation Problem. IEEE Int. Conf. Neural Networks&Signal Processing. December (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huan Tao
    • 1
  • Jian-yun Zhang
    • 1
  • Lin Yu
    • 1
  1. 1.Information Engineering Dept.Electronic Engineering InstituteHeFeiChina

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