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

Andrzej Cichocki, Shun Ichi Amari, Jianting Cao

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

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 for 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.

Original languageEnglish
Pages (from-to)1595-1601
Number of pages7
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE80-A
Issue number9
Publication statusPublished - 1997
Externally publishedYes

Keywords

  • Blind sources separation and deconvolution
  • Neural networks
  • On-line adaptive algorithms

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