A neural net for blind separation of nonstationary signals
Neural Networks
Adaptive blind separation of independent sources: a deflation approach
Signal Processing
A fast fixed-point algorithm for independent component analysis
Neural Computation
A neural learning algorithm for blind separation of sources based on geometric properties
Signal Processing - Special issue on neural networks
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Adaptive unsupervised extraction of one component of a linear mixture with a single neuron
IEEE Transactions on Neural Networks
Robust Blind Source Separation Utilizing Second and Fourth Order Statistics
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
An algorithm for extracting fetal electrocardiogram
Neurocomputing
Stability and Chaos of a Class of Learning Algorithms for ICA Neural Networks
Neural Processing Letters
Sequential Blind Signal Extraction with the Linear Predictor
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Linear prediction based blind source extraction algorithms in practical applications
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Stability and chaos analysis for an ICA algorithm
Computers & Mathematics with Applications
QML-based joint diagonalization of positive-definite hermitian matrices
IEEE Transactions on Signal Processing
Noisy component extraction (NoiCE)
IEEE Transactions on Circuits and Systems Part I: Regular Papers
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
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Most of the algorithms for blind separation/extraction and independent component analysis (ICA) can not separate mixtures of sources with extremely low kurtosis or colored Gaussian sources. Moreover, to separate mixtures of super- and sub-Gaussian signals, it is necessary to use adaptive (time-variable) or switching nonlinearities which are controlled via computationally intensive measures, such as estimation of the sign of kurtosis of extracted signals. In this paper, we develop a very simple neural network model and an efficient on-line adaptive algorithm that sequentially extract temporally correlated sources with arbitrary distributions, including colored Gaussian sources and sources with extremely low values (or even zero) of kurtosis. The validity and performance of the algorithm have been confirmed by extensive computer simulation experiments.