This paper presents a numerically stable fast Newton-type adaptive filter algorithm. Two problems are dealt with in the paper. First, we derive the proposed algorithm from an order-recursive least squares algorithm. T...
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This paper presents a numerically stable fast Newton-type adaptive filter algorithm. Two problems are dealt with in the paper. First, we derive the proposed algorithm from an order-recursive least squares algorithm. The result of the proposed algorithm is equivalent to that of the fast Newton transversal filter (FNTF) algorithm. However, the derivation process is different. Instead of extending a covariance matrix of the input based on the min-max and the max-min criteria, the derivation shown in this paper is to solve an optimum extension problem of the gain vector based on the information of the Mth-order forward or backward predictor. The derivation provides an intuitive explanation of the FNTF algorithm, which may be easier to understand. Second, we present stability analysis of the proposed algorithm using a linear time-variant state-space method. We show that the proposed algorithm has a well-analyzable stability structure, which is indicated by a transition matrix. The eigenvalues of the ensemble average of the transition matrix are proved all to be asymptotically less than unity. This results in a much-improved numerical performance of the proposed algorithm compared with the combination of the stabilized fast recursive least squares (Sfrls) and the FNTF algorithms. Computer simulations implemented by using a finite-precision arithmetic have confirmed the validity of our analysis.
In this paper, we present a detailed study of the perfectly predictable signals in the case of pure sinusoids, in order to determine the cause of instability of the fast recursive least squares (frls) algorithms with ...
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ISBN:
(纸本)9781424407255
In this paper, we present a detailed study of the perfectly predictable signals in the case of pure sinusoids, in order to determine the cause of instability of the fast recursive least squares (frls) algorithms with speech signal. To avoid instability of the frls algorithm, an assigned choice of the sampling rate compared to the frequency of the sinusoid is proposed. Tests were carried out on pure sinusoids in the very long term without any tendency to divergence.
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