Robust Regularization of the Recusive Least-Squares Algorithm

被引：0

作者：

Paleologu, Constantin ^{[1
]}

Benesty, Jacob ^{[2
]}

Stanciu, Cristian ^{[1
]}

Anghel, Cristian ^{[1
]}

Stenta, Mircea ^{[1
]}

机构：

[1] Univ Politehn Bucuresti, Bucharest, Romania

[2] Univ Quebec, INRS EMT, Montreal, PQ, Canada

来源：

2016 8TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE (ECAI) | 2016年

关键词：

Adaptive filters; echo cancellation; regularization; recursive least-squares (RLS) algorithm; regularized RLS algorithm;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The regularization is mandatory in all ill-posed problems, especially in the presence of additive noise. In this paper, we consider the regularized recursive least-squares (RLS) algorithm and present a method to find its regularization parameter, depending on the signal-to-noise ratio. We also outline the robustness features of this solution, which could make the RLS algorithm to behave well even in non-stationary environments. Simulations performed in the context of echo cancellation support these findings.

引用

页数：4

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