Gradient Adaptive Algorithms for Contrast-Based Blind Deconvolution
Journal of VLSI Signal Processing Systems
Journal of VLSI Signal Processing Systems
EURASIP Journal on Applied Signal Processing
FNN (Feedforward Neural Network) Training Method Based on Robust Recursive Least Square Method
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A Simple Adaptive Algorithm for Principle Component and Independent Component Analysis
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Sequential extraction algorithm for BSS without error accumulation
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Robust recursive complex extreme learning machine algorithm for finite numerical precision
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A prewhitening RLS projection alternated subspace tracking (PAST) algorithm
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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We analyze and extend a class of adaptive networks for second-order blind decorrelation of instantaneous signal mixtures. First, we compare the performance of the single-layer neural network employing global knowledge of the adaptive coefficients with a similar structure whose coefficients are adapted via local output connections. Through statistical analyzes, the convergence behaviors and stability bounds for the algorithms' step sizes are studied and derived. Second, we analyze the behaviors of locally adaptive multilayer decorrelation networks and quantify their performances for poorly conditioned signal mixtures. Third, we derive a robust locally adaptive network structure based on a posteriori output signals that remains stable for any step-size value. Finally, we present an extension of the locally adaptive network for linear-phase temporal and spatial whitening of multichannel signals. Simulations verify the analyses and indicate the usefulness of the locally adaptive networks for decorrelating signals in space and time