Fuzzy-based learning rate determination for blind source separation

Many independent component analysis (ICA) algorithms have been proposed for blind source separation. These algorithms belong to the LMS-type algorithm in natural. Hence, the choice of the step-size reflects a tradeoff between misadjustment and the speed of convergence. Based on the separation state of outputs of the neural network for ICA, the paper develops a fuzzy inference-based step-size selection algorithm. The fuzzy inference system consists of two inputs (the second- and higher order correlation coefficients of output components) and one output (the fuzzy learning rate). In this way, the ICA algorithms become more efficient, which is verified by simulation results.

[1]  H. H. Yang,et al.  Serial updating rule for blind separation derived from the method of scoring , 1999, IEEE Trans. Signal Process..

[2]  Shun-ichi Amari,et al.  Self-adaptive neural networks for blind separation of sources , 1996, 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96.

[3]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Andrzej Cichocki,et al.  Robust neural networks with on-line learning for blind identification and blind separation of sources , 1996 .

[5]  Shun-ichi Amari,et al.  Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.

[6]  Chin-Teng Lin,et al.  An ART-based fuzzy adaptive learning control network , 1997, IEEE Trans. Fuzzy Syst..

[7]  Juha Karhunen,et al.  On Neural Blind Separation with Noise Suppression and Redundancy Reduction , 1997, Int. J. Neural Syst..

[8]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[9]  Woon-Seng Gan.  Fuzzy step-size adjustment for the LMS algorithm , 1996, Signal Process..

[10]  Shun-ichi Amari,et al.  Adaptive blind signal processing-neural network approaches , 1998, Proc. IEEE.

[11]  Andreas Ziehe,et al.  Adaptive On-line Learning in Changing Environments , 1996, NIPS.

[12]  Ralph Linsker,et al.  An Application of the Principle of Maximum Information Preservation to Linear Systems , 1988, NIPS.

[13]  Andrzej Cichocki,et al.  Flexible Independent Component Analysis , 2000, J. VLSI Signal Process..

[14]  Shun-ichi Amari,et al.  Adaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information , 1997, Neural Computation.

[15]  Andrzej Cichocki,et al.  Information-theoretic approach to blind separation of sources in non-linear mixture , 1998, Signal Process..

[16]  Ruey-Wen Liu,et al.  General approach to blind source separation , 1996, IEEE Trans. Signal Process..

[17]  S. Amari.  A Theory ofAdaptive Pattern Classifiers , 1967 .

[18]  S. Amari,et al.  Flexible Independent Component Analysis , 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).

[19]  Juha Karhunen,et al.  Neural networks for blind separation with unknown number of sources , 1999, Neurocomputing.

[20]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[21]  V. John Mathews,et al.  A stochastic gradient adaptive filter with gradient adaptive step size , 1993, IEEE Trans. Signal Process..

[22]  W. Y. Chen,et al.  A variable step size LMS algorithm , 1990, Proceedings of the 33rd Midwest Symposium on Circuits and Systems.

[23]  Sergio Cruces,et al.  An iterative inversion approach to blind source separation , 2000, IEEE Trans. Neural Networks Learn. Syst..

[24]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[25]  Lang Tong,et al.  Indeterminacy and identifiability of blind identification , 1991 .

[26]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .

[27]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[28]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[29]  Shun-ichi Amari,et al.  A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..

[30]  Richard W. Harris,et al.  A variable step (VS) adaptive filter algorithm , 1986, IEEE Trans. Acoust. Speech Signal Process..

[31]  S.C. Douglas,et al.  Adaptive step size techniques for decorrelation and blind source separation , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).