This paper presents a stochastic model for the normalized smoothed variation rate individual-activation-factor proportionatenormalizedleast-mean-square (NSVR-IAF-PNLMS) algorithm. Specifically, taking into account c...
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This paper presents a stochastic model for the normalized smoothed variation rate individual-activation-factor proportionatenormalizedleast-mean-square (NSVR-IAF-PNLMS) algorithm. Specifically, taking into account correlated Gaussian input data, model expressions are derived for predicting the mean weight vector, gain distribution matrix, NSVR metric, learning curve, weight-error correlation matrix, and steady-state excess mean-square error. Such expressions are obtained by considering the time-varying characteristics of the gain distribution matrix. Simulation results are shown confirming the accuracy of the proposed model for different operating conditions.
proportionate-type algorithms are designed to exploit the sparseness character of the systems to be identified, in order to improve the overall convergence of the adaptive filters used in this context. However, when t...
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ISBN:
(纸本)9781538646953
proportionate-type algorithms are designed to exploit the sparseness character of the systems to be identified, in order to improve the overall convergence of the adaptive filters used in this context. However, when the parameter space is large, the system identification problem becomes more challenging. In this paper, we focus on the identification of bilinear forms, where the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In this framework, we develop a proportionate normalized least-mean-square algorithm tailored for the identification of such bilinear forms. Simulation results indicate the good performance of the proposed algorithm, in terms of both convergence rate and computational complexity.
This letter presents model expressions describing the steady-state behavior of proportionatenormalizedleast-mean-square (PNLMS)-type algorithms, taking into account both complex- and real-valued correlated Gaussian ...
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This letter presents model expressions describing the steady-state behavior of proportionatenormalizedleast-mean-square (PNLMS)-type algorithms, taking into account both complex- and real-valued correlated Gaussian input data. Specifically, based on energy-conservation arguments, general expressions for the excess mean-square error (EMSE) in steady state and misadjustment are obtained. Such general expressions are then applied to two well-known PNLMS-type algorithms, namely the improved PNLMS (IPNLMS) and the individual-activation-factor PNLMS (IAF-PNLMS). Simulation results are shown confirming the accuracy of the proposed model expressions under different operating conditions.
This paper proposes a novel adaptive filtering scheme named the Krylov-proportionatenormalizedleast-mean-square (KPNLMS) algorithm. KPNLMS exploits the benefits (i.e., fast convergence for sparse unknown systems) of...
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This paper proposes a novel adaptive filtering scheme named the Krylov-proportionatenormalizedleast-mean-square (KPNLMS) algorithm. KPNLMS exploits the benefits (i.e., fast convergence for sparse unknown systems) of the proportionate NLMS algorithm, but its applications are not limited to sparse unknown systems. A set of orthonormal basis vectors is generated from a certain Krylov sequence. It is proven that the unknown system is sparse with respect to the basis vectors in case of fairly uncorrelated input data. Different adaptation gain is allocated to a coefficient of each basis vector, and the gain is roughly proportional to the absolute value of the corresponding coefficient of the current estimate. KPNLMS enjoys i) fast convergence, ii) linear complexity per iteration, and iii) no use of any a priori information. Numerical examples demonstrate significant advantages of the proposed scheme over the reduced-rank method based on the multistage Wiener filter (MWF) and the transform-domain adaptive filter (TDAF) both in noisy and silent situations.
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