Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Natural gradient works efficiently in learning
Neural Computation
Independent component analysis: theory and applications
Independent component analysis: theory and applications
On-line learning and stochastic approximations
On-line learning in neural networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation
Neural Computation
A stochastic gradient adaptive filter with gradient adaptive stepsize
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Local coupled feedforward neural network
Neural Networks
A parallel dual matrix method for blind signal separation
Neural Computation
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We propose an adaptive improved natural gradient algorithm for blind separation of independent sources. First, inspired by the well-known backpropagation algorithm, we incorporate a momentum term into the natural gradient learning process to accelerate the convergence rate and improve the stability. Then an estimation function for the adaptation of the separation model is obtained to adaptively control a step-size parameter and a momentum factor. The proposed natural gradient algorithm with variable step-size parameter and variable momentum factor is therefore particularly well suited to blind source separation in a time-varying environment, such as an abruptly changing mixing matrix or signal power. The expected improvement in the convergence speed, stability, and tracking ability of the proposed algorithm is demonstrated by extensive simulation results in both time-invariant and time-varying environments. The ability of the proposed algorithm to separate extremely weak or badly scaled sources is also verified. In addition, simulation results show that the proposed algorithm is suitable for separating mixtures of many sources (e.g., the number of sources is 10) in the complete case.