Blind Signal Extraction of Arbitrarily Distributed, but Temporally Correlated Signals : A Neural Network Approach (Special Section on Nonlinear Theory and Its Applications)

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概要

• 論文の詳細を見る

• 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.

• 社団法人電子情報通信学会の論文
• 1999-09-25

著者

• Cichocki A

  Riken Saitama Jpn

• THAWONMAS Ruck

  Department of Information Systems Engineering, Kochi University of Technology

• CICHOCKI Andrzej

  Laboratory for Open Information Systems, Brain Science Institute, RIKEN

• Cichocki Andrzej

  Laboratory For Advanced Brain Signal Processing Brain Science Institute Riken (the Institute Of Phys

• Thawonmas Ruck

  Department Of Information Systems Engineering Kochi University Of Technology

関連論文

• Blind Signal Extraction of Arbitrarily Distributed, but Temporally Correlated Signals : A Neural Network Approach (Special Section on Nonlinear Theory and Its Applications)
• Speech comprehension assessed by electroencephalography : A new method using m-sequence modulation

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