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

Producción científica: Article › revisión exhaustiva

10 Citas (Scopus)

Resumen

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.

Idioma original	English
Páginas (desde-hasta)	715-735
Número de páginas	21
Publicación	International Journal of Adaptive Control and Signal Processing
Volumen	30
N.º	5
DOI	https://doi.org/10.1002/acs.2634
Estado	Published - may 1 2016

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

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Electrical and Electronic Engineering

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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 - 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

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ASJC Scopus subject areas

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