Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
A stochastic gradient adaptive filter with gradient adaptive stepsize
IEEE Transactions on Signal Processing
Universal linear prediction by model order weighting
IEEE Transactions on Signal Processing
Mean-square performance of a convex combination of two adaptive filters
IEEE Transactions on Signal Processing
A robust variable step-size LMS-type algorithm: analysis andsimulations
IEEE Transactions on Signal Processing
Digital communication receivers using gaussian processes for machine learning
EURASIP Journal on Advances in Signal Processing
Journal of Signal Processing Systems
Adaptive combination of proportionate filters for sparse echo cancellation
IEEE Transactions on Audio, Speech, and Language Processing
On proportionate-type NLMS algorithms for fast decay of output error at all times
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Adaptively biasing the weights of adaptive filters
IEEE Transactions on Signal Processing
Adaptive combination of subband adaptive filters with selective partial updates
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Adaptive mixture methods based on Bregman divergences
Digital Signal Processing
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For least mean-square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade-off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during stationary periods. Some plant identification simulation examples show the effectiveness of our method when compared to previous variable step size approaches. This combination approach can be straightforwardly extended to other kinds of filters, as it is illustrated with a convex combination of recursive least-squares (RLS) filters.