A Variable Step-Size for Sparse Nonlinear Adaptive Filters

被引：0

作者：

Carini, Alberto ^{[1
]}

Lima, Markus V. S. ^{[2
]}

Yazdanpanah, Hamed ^{[3
]}

Orcioni, Simone ^{[4
]}

Cecchi, Stefania ^{[4
]}

机构：

[1] Univ Trieste, Trieste, Italy

[2] Fed Univ Rio de Janeiro UFRJ, Rio De Janeiro, Brazil

[3] Univ Sao Paulo, Sao Paulo, Brazil

[4] Univ Politecn Marche, Ancona, Italy

来源：

28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020) | 2021年

关键词：

Adaptive filters; linear-in-the-parameters nonlinear filters; functional link polynomial filters; optimal step-size;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

The paper deals with the identification of nonlinear systems with adaptive filters. In particular, adaptive filters for functional link polynomial (FLiP) filters, a broad class of linear-in-the-parameters (LIP) nonlinear filters, are considered. FLiP filters include many popular LIP filters, as the Volterra filters, the Wiener nonlinear filters, and many others. Given the large number of coefficients of these filters modeling real systems, especially for high orders, the solution is often very sparse. Thus, an adaptive filter exploiting sparsity is considered, the improved proportionate NLMS algorithm (IPNLMS), and an optimal stepsize is obtained for the filter. The optimal step-size alters the characteristics of the IPNLMS algorithm and provides a novel gradient descent adaptive filter. Simulation results involving the identification of a real nonlinear device illustrate the achievable performance in comparison with competing similar approaches.

引用

页码：2383 / 2387

页数：5