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

Asad A. Ali; Jesse B. Hoagg; Magnus Mossberg; Dennis S. Bernstein

doi:10.1002/acs.2634

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

Asad A. Ali, Jesse B. Hoagg, Magnus Mossberg, Dennis S. Bernstein

Mechanical and Aerospace Engineering

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	715-735
Number of pages	21
Journal	International Journal of Adaptive Control and Signal Processing
Volume	30
Issue number	5
DOIs	https://doi.org/10.1002/acs.2634
State	Published - May 1 2016

Bibliographical note

Publisher Copyright:
© Copyright 2015 John Wiley & Sons, Ltd.

Keywords

digital signal processing
sliding-window RLS
variable regularization

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Electrical and Electronic Engineering

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10.1002/acs.2634

Cite this

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abstract = "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.",

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AU - Mossberg, Magnus

AU - Bernstein, Dennis S.

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AB - 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.

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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