Grading learning for blind source separation
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By generalizing the learning rate parameter to a learning rate matrix, this paper proposes a grading learning algorithm for blind source separation. The whole learning process is divided into three stages: initial stage, capturing stage and tracking stage. In different stages, different learning rates are used for each output component, which is determined by its dependency on other output components. It is shown that the grading learning algorithm is equivariant and can keep the separating matrix from becoming singular. Simulations show that the proposed algorithm can achieve faster convergence, better steady-state performance and higher numerical robustness, as compared with the existing algorithms using fixed, time-descending and adaptive learning rates.
Keywordsblind source separation independent component analysis neural computation adaptive learning
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- 2.Giannakopoulos, V., Comparison of adaptive independent component analysis algorithms, Available at http://www.cis.hut.fi/~xgiannak/.Google Scholar
- 11.Leen, T. W., Girolami, M., Sejnowski, T. J., Independent component analysis using an extended informax algorithm for mixed sub-Gaussian and super-Gaussian sources, Neural Computations, 1999, 11: 409–433.Google Scholar
- 14.Amari, S., Cichocki, A., Yang, H. H., A new learning algorithm for blind signal separation, in Advances in NIPS, 1996, 8: 757–763.Google Scholar
- 16.Cichocki, A., Amari, S., Adachi, M. et al., Self-adaptive neural networks for blind separation of sources, Proc. 1996 International Symp. on Circuits and Systems, vol.2, New York: IEEE Press, May 1996, 157–160.Google Scholar
- 17.Murata, N., Muller, K. R., Ziehe, A. et al., Adaptive on-line learning in changing environments. Advances in NIPS’9, Cambridge, MA: MIT Press, 1997, 599–605.Google Scholar
- 18.Douglas, S. C., Cichocki, A., Adaptive step size techniques for decorrelation and blind source separation, Proc. 32nd Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, vol.2, New York: IEEE Press, Nov. 1998, 1191–1195.Google Scholar
- 21.Zhang, X. D., Bao, Z., Communication Signal Processing (in Chinese), Beijing: Defense Industry Press, 2000, 367–383.Google Scholar
- 22.Papoulis, A., Probability, Random Variables, and Stochastic Process, 3rd edition, New York: McGraw-Hill, 1991, 190–191.Google Scholar