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On the stability and convergence of a sliding‐window variable‐regularization recursive‐least‐squares algorithm
Author(s) -
Ali Asad A.,
Hoagg Jesse B.,
Mossberg Magnus,
Bernstein Dennis S.
Publication year - 2016
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2634
Subject(s) - weighting , algorithm , regularization (linguistics) , mathematics , stability (learning theory) , convergence (economics) , least squares function approximation , sliding window protocol , computer science , window (computing) , artificial intelligence , statistics , medicine , machine learning , estimator , economics , radiology , economic growth , operating system
Summary 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 © 2015 John Wiley & Sons, Ltd.