This paper studies the stochastic behavior of the signed variants of the lms algorithm for a system identification framework when the input signal is a cyclostationary white Gaussian process. Three algorithms are stud...
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This paper studies the stochastic behavior of the signed variants of the lms algorithm for a system identification framework when the input signal is a cyclostationary white Gaussian process. Three algorithms are studied: the signed regressor, the signed error, and the sign-sign algorithms. The input cyclostationary signal is modeled by a white Gaussian random process with periodically time-varying power. The system parameters vary according to a random-walk. Mathematical models are derived for the mean and mean-square-deviation behavior of the adaptive weights with the input cyclostationarity. These models are used to derive new results concerning the performance of the algorithms. Some of these results are surprising. Monte Carlo simulations of the three algorithms provide strong support for the theory.
作者：
Gao, WeiChen, JieJiangsu Univ
Sch Comp Sci & Telecommun Engn Zhenjiang 212013 Jiangsu Peoples R China Northwestern Polytech Univ
Ctr Intelligent Acoust & Immers Commun Sch Marine Sci & Technol Xian 710072 Peoples R China
It is of significant importance to investigate the transient behaviors of signed lms algorithms under the cyclostationary colored Gaussian inputs for the design of corresponding filters in practical applications. In t...
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It is of significant importance to investigate the transient behaviors of signed lms algorithms under the cyclostationary colored Gaussian inputs for the design of corresponding filters in practical applications. In this brief, we derive the analytical models of a family of signed lms algorithms with the cyclostationary colored Gaussian inputs in the mean and mean-square sense for the nonstationary system identification. Simulation results illustrate the excellent consistency between the simulated results and the theoretical findings.
The analysis of saturation-type nonlinearities on the input and the error in the weight update equation for lms adaptation were obtained for a stationary white Gaussian data model in [28] for system identification. He...
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The analysis of saturation-type nonlinearities on the input and the error in the weight update equation for lms adaptation were obtained for a stationary white Gaussian data model in [28] for system identification. Here the input signal is modeled by a cyclostationary white Gaussian random process with periodically time-varying power. The system parameters vary according to a random-walk. Using the previous analysis results, nonlinear recursions are presented for the transient and steady-state weight first and second moments that include the effect of the soft limiters. Monte Carlo simulations of the algorithms provide strong support for the theory. (C) 2018 Elsevier B.V. All rights reserved.
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