Self-adaptive independent component analysis for sub-Gaussian and super-Gaussian mixtures with an un

| In this paper we derive and analyze un-supervised adaptive on-line algorithms for instantaneous blind separation of sources (BSS) in the case when sensors signals are noisy and they are mixture of unknown number of independent source signals with unknown statistics. Nonlinear activation functions are rigorously derived assuming that source have generalized Gaussian, Cauchy o r R a yleigh distributions. Extensive computer simulations conrmed that the proposed family of learning algorithms are able to separate sources from mixture of sub and super-Gaussian sources.

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

[2]  Kiyotoshi Matsuoka,et al.  A neural net for blind separation of nonstationary signals , 1995, Neural Networks.

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

[4]  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.

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

[6]  O. Macchi,et al.  Adaptive separation of an unknown number of sources , 1997, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics.

[7]  T. Sato,et al.  On-line algorithms for blind deconvolution of multichannel linear time-invariant systems , 1997, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics.

[8]  Shun-ichi Amari,et al.  Stability Analysis Of Adaptive Blind Source Separation , 1997 .

[9]  S. Amari,et al.  Multichannel blind separation and deconvolution of sources with arbitrary distributions , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[10]  S. Amari.  Natural Gradient Works Eciently in Learning , 2022 .