Space or time adaptive signal processing by neural network models
AIP Conference Proceedings 151 on Neural Networks for Computing
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive blind separation with an unknown number of sources
Neural Computation
Blind source separation using order statistics
Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
General approach to blind source separation
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Sequential blind extraction of instantaneously mixed sources
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
Fuzzy-based learning rate determination for blind source separation
IEEE Transactions on Fuzzy Systems
A Markov model for blind image separation by a mean-field EM algorithm
IEEE Transactions on Image Processing
Fast probabilistic self-structuring of generalized single-layer networks
IEEE Transactions on Neural Networks
A robust approach to independent component analysis of signals with high-level noise measurements
IEEE Transactions on Neural Networks
Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks
IEEE Transactions on Neural Networks
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This paper focuses on blind source separation with an unknown number of sources, which is the case generally assumed in most practical applications. Several over-determined neural algorithms (more sensors m than sources n) have been proposed to solve the problems associated with these cases, but separating performance is often sacrificed in order to prevent divergence. The general natural gradient descent can be validly applied to determined algorithms (m=n) only. Therefore, to better solve the problems, an algorithm associating the feed-forward neural network and an auto-trimming technique is proposed. The learning process starts with an over-determined architecture, followed by two steps used in every iteration. First, the number of sources is estimated by using the stability discriminant function, next, the neural network gradually trims redundant nodes according to an instant estimation. Validity and performance of the proposed approaches are demonstrated with computer simulations on artificially synthesized signals and compared with the well-known algorithm proposed by Ye et al.