A noise resilient variable step-size LMS algorithm
Signal Processing
Variable step-size LMS algorithm with a quotient form
Signal Processing
Krylov-proportionate adaptive filtering techniques not limited to sparse systems
IEEE Transactions on Signal Processing
A robust extended Elman backpropagation algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
On the weight convergence of Elman networks
IEEE Transactions on Neural Networks
A variable step-size selective partial update LMS algorithm
Digital Signal Processing
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A class of new adaptive step-size control algorithms, which is applicable to most of the LMS-derived tap weight adaptation algorithms, is proposed. Analysis yields a set of difference equations for theoretically calculating the transient behavior of the filter convergence and derives an explicit formula for the steady-state excess mean-square error (EMSE). Experiments for some examples prove that the proposed algorithm is highly effective in improving the convergence rate in both transient and tracking phases. The theoretically calculated convergence is shown to be in good agreement with that obtained through simulations. Alternative formulae of the step-size adaptation for specific tap weight adaptation algorithms are also proposed