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)
Affiliated Institutions
Related Publications
Independent Component Analysis
A tutorial-style introduction to a class of methods for extracting independent signals from a mixture of signals originating from different physical sources; includes MatLab com...
A new optimizer using particle swarm theory
The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently develope...
Dropout Training of Matrix Factorization and Autoencoder for Link Prediction in Sparse Graphs
Matrix factorization (MF) and Autoencoder (AE) are among the most successful approaches of unsupervised learning. While MF based models have been extensively exploited in the gr...
Hidden Conditional Random Fields
We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-st...
Global optimization of a neural network-hidden Markov model hybrid
An original method for integrating artificial neural networks (ANN) with hidden Markov models (HMM) is proposed. ANNs are suitable for performing phonetic classification, wherea...
Publication Info
- Year
- 1996
- Type
- article
- Volume
- 43
- Issue
- 11
- Pages
- 894-906
- Citations
- 259
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
259
OpenAlex
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