On the stability and convergence of a sliding-window variable-regularization recursive-least-squares algorithm

被引：8
|
作者：
Ali, Asad A. [1 ]
Hoagg, Jesse B. [2 ]
Mossberg, Magnus [3 ]
Bernstein, Dennis S. [1 ]
机构：
[1] Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA
[2] Univ Kentucky, Dept Mech Engn, 151 Ralph G Anderson Bldg, Lexington, KY 40507 USA
[3] Karlstad Univ, Dept Engn & Phys, SE-65188 Karlstad, Sweden
来源：
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING | 2016年 / 30卷 / 05期
关键词：
variable regularization; sliding-window RLS; digital signal processing; RLS;
D O I：
10.1002/acs.2634
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A sliding-window variable-regularization recursive-least-squares algorithm is derived, and its convergence properties, computational complexity, and numerical stability are analyzed. The algorithm operates on a finite data window and allows for time-varying regularization in the weighting and the difference between estimates. Numerical examples are provided to compare the performance of this technique with the least mean squares and affine projection algorithms. Copyright (c) 2015 John Wiley & Sons, Ltd.
引用
收藏
页码：715 / 735
页数：21
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