最小均方滤波器
正规化(语言学)
计算机科学
趋同(经济学)
算法
系统标识
自适应滤波器
估计理论
数学
控制理论(社会学)
数学优化
人工智能
数据建模
经济增长
数据库
经济
控制(管理)
作者
Danqi Jin,Jie Chen,Cédric Richard,Jingdong Chen
标识
DOI:10.1016/j.sigpro.2018.06.020
摘要
Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. Similarly to most adaptive filtering algorithms and sparsity-inducing regularization techniques, ZA-LMS appears to face a trade-off between convergence speed and steady-state performance, and between sparsity level and estimation bias. It is therefore important, but not trivial, to optimally set the algorithm parameters. To address this issue, a variable-parameter ZA-LMS algorithm is proposed in this paper, based on a model of the stochastic transient behavior of the ZA-LMS. By minimizing the excess mean-square error (EMSE) at each iteration on the basis of a white input assumption, we obtain closed-form expression of the step-size and regularization parameter. To improve the performance, we introduce the same strategy for the reweighted ZA-LMS (RZA-LMS). Simulation results illustrate the effectiveness of the proposed algorithms and highlight their performance through comparisons with state-of-the-art algorithms, in the case of white and correlated inputs.
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