NDLI: Sliding window recursive quadratic optimization with variable regularization

Content Provider	IEEE Xplore Digital Library
Author	Hoagg, J.B. Ali, A.A. Mossberg, M. Bernstein, D.S.
Copyright Year	2011
Description	Author affiliation: Dept. of Mech. Eng., Univ. of Kentucky, Lexington, KY, USA (Hoagg, J.B.) \|\| Dept. of Aerosp. Eng., Univ. of Michigan, Ann Arbor, MI, USA (Ali, A.A.; Bernstein, D.S.) \|\| Dept. of Phys. & Electri cal Eng., Karlstad Univ., Karlstad, Sweden (Mossberg, M.)
Abstract	In this paper, we present a sliding-window variable-regularization recursive least squares algorithm. In contrast to standard recursive least squares, the algorithm presented in this paper operates on a finite window of data, where old data are discarded as new data become available. This property can be beneficial for estimating time-varying parameters. Furthermore, standard recursive least squares uses time-invariant regularization. More specifically, the inverse of the initial covariance matrix in standard recursive least squares can be viewed as a regularization term, which weights the difference between the next estimate and the initial estimate. This regularization is fixed for all steps of the recursion. The algorithm derived in this paper allows for time-varying regularization. In particular, the present paper allows for time varying regularization in the weighting as well as what is being weighted. Specifically, the regularization term can weight the difference between the next estimate and a time-varying vector of parameters rather than the initial estimate.
Sponsorship	Control Syst. Soc.
Starting Page	3275
Ending Page	3280
File Size	224549
Page Count	6
File Format	PDF
ISBN	9781457700804
ISSN	07431619
e-ISBN	9781457700811
DOI	10.1109/ACC.2011.5991159
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Convergence Signal processing algorithms Signal to noise ratio Electronic mail Covariance matrix Equations
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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