Abstract
Two unsupervised, self-normalizing, adaptive learning algorithms are developed for robust blind identification and/or blind separation of independent source signals from a linear mixture of them. One of these algorithms is developed for on-line learning of a single-layer feed-forward neural network model and a second one for a feedback (fully recurrent) neural network model. The proposed algorithms are robust, efficient, fast and suitable for real-time implementations. Moreover, they ensure the separation of extremely weak or badly scaled stationary signals, as well as a successful separation even if the mixture matrix is very ill-conditioned (near singular). The performance of the proposed algorithms is illustrated by computer simulation experiments.
Keywords
Blind signal separationArtificial neural networkComputer scienceIdentification (biology)Artificial intelligenceAlgorithmIndependent component analysisSeparation (statistics)Matrix (chemical analysis)Unsupervised learningLine (geometry)Machine learningMathematicsChannel (broadcasting)
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Publication Info
- Year
- 1996
- Type
- article
- Volume
- 43
- Issue
- 11
- Pages
- 894-906
- Citations
- 259
- Access
- Closed
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Cite This
Andrzej Cichocki,
R. Unbehauen
(1996).
Robust neural networks with on-line learning for blind identification and blind separation of sources.
IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications
, 43
(11)
, 894-906.
https://doi.org/10.1109/81.542280
Identifiers
- DOI
- 10.1109/81.542280