A network model for blind source extraction in various ill-conditioned cases

Authors:
Yuanqing Li;Jun Wang
Affiliations:
Institute of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China;Department of Automation and Computer-Aided Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
Venue:
Neural Networks
Year:
2005

Citing 19
Cited 2

Blind separation of sources, Part 1: an adaptive algorithm based on neuromimetic architecture

Signal Processing
Blind separation of sources, Part II: problems statement

Signal Processing
Blind separation of sources, Part III: stability analysis

Signal Processing
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
Adaptive blind separation of independent sources: a deflation approach

Signal Processing
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Adaptive online learning algorithms for blind separation: maximum entropy and minimum mutual information

Neural Computation
Blind separation of sources using higher-order cumulants

Signal Processing
Independent component analysis: algorithms and applications

Neural Networks
Blind Extraction of Source Signals with Specified Stochastic Features

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Self-adaptive source separation. II. Comparison of the direct,feedback, and mixed linear network

IEEE Transactions on Signal Processing
General approach to blind source separation

IEEE Transactions on Signal Processing
A novel blind deconvolution scheme for image restoration usingrecursive filtering

IEEE Transactions on Signal Processing
Sequential blind extraction of instantaneously mixed sources

IEEE Transactions on Signal Processing
Fourth-order criteria for blind sources separation

IEEE Transactions on Signal Processing
Equivariant adaptive source separation

IEEE Transactions on Signal Processing
Neural networks for blind decorrelation of signals

IEEE Transactions on Signal Processing
Adaptive unsupervised extraction of one component of a linear mixture with a single neuron

IEEE Transactions on Neural Networks
Blind extraction of singularly mixed source signals

IEEE Transactions on Neural Networks

Underdetermined blind separation of non-sparse sources using spatial time-frequency distributions

Digital Signal Processing
A new blind method for separating M+1 sources from M mixtures

Computers & Mathematics with Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper discusses blind source extraction in various ill-conditioned cases based on a simple extraction network model. Extractability is first analyzed for the following ill-conditioned cases: the mixing matrix is square but singular, the number of sensors is smaller than that of sources, the number of sensors is larger than that of sources but the column rank of mixing matrix is deficient, and the number of sources is unknown and the column rank of mixing matrix is deficient. A necessary and sufficient condition for extractability is obtained. A cost function and an unsupervised learning algorithm for the extraction network model are developed. Simulation results are also presented to show the validity of the theoretical results and the performance and characteristics of the learning algorithm.