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Blind signal extraction of arbitrarily distributed, but temporally correlated signals - A neural network approach

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1999年第9期E82A卷 1834-1844页

作者： Thawonmas, R Cichocki, A Kochi Univ Technol Dept Informat Syst Engn Kochi 7828502 Japan RIKEN Brain Sci Inst Lab Open Informat Syst Wako Saitama 3510198 Japan

In this paper, we discuss a neural network approach for blind signal extraction of temporally correlated sources. Assuming autoregressive models of source signals, we propose a very simple neural network model and an efficient online adaptive algorithm that extract, from linear mixtures, a temporally correlated source with an arbitrary distribution, including a colored Gaussian source and a source with extremely low value (or even zero) of kurtosis. We then combine these extraction processing units with deflation processing units to extract such sources sequentially in a cascade fashion. Theory and simulations show that the proposed neural network successfully extracts all arbitrarily distributed, but temporally correlated source signals from linear mixtures.

关键词： blind source separation and extraction neural networks on-line adaptive algorithms

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A cascade neural network for blind signal extraction without spurious equilibria

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1998年第9期E81A卷 1833-1846页

作者： Thawonmas, R Cichocki, A Amari, S RIKEN Brain Sci Inst Brain Style Informat Syst Res Grp Wako Saitama 3510198 Japan

We present a cascade neural network for blind source extraction. We propose a family of unconstrained optimization criteria, from which we derive a learning rule that can extract a single source signal from a linear mixture of source signals. To prevent the newly extracted source signal from being extracted again in the next processing unit, we propose another unconstrained optimization criterion that uses knowledge of this signal. From this criterion, we then derive a learning rule that deflates from the mixture the newly extracted signal. By virtue of blind extraction and deflation processing, the presented cascade neural network can cope with a practical case where the number of mixed signals is equal to or larger than the number of sources, with the number of sources not known in advance. We prove analytically that the proposed criteria both for blind extraction and deflation processing have no spurious equilibria. In addition, the proposed criteria do not require whitening of mixed signals. We also demonstrate the validity and performance of the presented neural network by computer simulation experiments.

关键词： blind source separation and extraction neural networks on-line adaptive algorithms

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Neural network models for blind separation of time delayed and convolved signals

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1997年第9期E80A卷 1595-1603页

作者： Cichocki, A Amari, S Cao, JT Brain Information Processing Group Frontier Research Program RIKEN Wako-shi 351-01 Japan

In this paper we develop a new family of on-line adaptive learning algorithms for blind separation of time delayed and convolved sources. The algorithms are derived fbr feedforward and fully connected feedback (recurrent) neural networks on basis of modified natural gradient approach. The proposed algorithms can be considered as generalization and extension of existing algorithms for instantaneous mixture of unknown source signals. Preliminary computer simulations confirm validity and high performance of the proposed algorithms.

关键词： blind sources separation and deconvolution neural networks on-line adaptive algorithms

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