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.
|Number of pages||21|
|Journal||International Journal of Adaptive Control and Signal Processing|
|State||Published - May 1 2016|
Bibliographical notePublisher Copyright:
© Copyright 2015 John Wiley & Sons, Ltd.
- digital signal processing
- sliding-window RLS
- variable regularization
ASJC Scopus subject areas
- Control and Systems Engineering
- Signal Processing
- Electrical and Electronic Engineering