Adaptive filter theory
IEEE Transactions on Signal Processing
On the convergence behavior of the LMS and the normalized LMSalgorithms
IEEE Transactions on Signal Processing
Fast Newton transversal filters-a new class of adaptive estimationalgorithms
IEEE Transactions on Signal Processing
Simplified Newton-type adaptive estimation algorithms
IEEE Transactions on Signal Processing
Fast algorithms with low complexity for adaptive filtering
WSEAS Transactions on Signal Processing
Advanced algorithms for adaptive filtering
ISPRA'09 Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation
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Fast RLS algorithms are known to present numerical instability and this instability is originated in the backward prediction parameters. In this paper, a simplified FTF-type algorithm for adaptive filtering is presented. The basic idea behind the proposed algorithm is to avoid using the backward variables. By using only forward prediction variables and adding a small regularization constant and a leakage factor, we obtain a robust numerically stable FTF-type algorithm that shows the same performances as the numerically stable FTF algorithms. The computational complexity of the proposed algorithm is 7N when used with a full size predictor, which is less complex than the 8N numerically stable FTF algorithms and this computational complexity can be significantly reduced to 2N+5P when used with a reduced P-size forward predictor.