Asymptotically Optimal Smoothing of Stochastic Approximation Estimates for Regression Parameter Tracking
2001 (English)Report (Other academic)
The sequence of estimates formed by the LMS algorithm for a standard linear regression estimation problem are considered. It is known since earlier that smoothing these estimates by simple averaging will lead to, asymptotically, the recursive least squares algorithm. In this paper it is first shown that smoothing the LMS estimates using amatrix updating will lead to smoothed estimates with optimal tracking properties, also in the case the true parameters are changing as a random walk. The choice of smoothing matrix should be tailored to the properties of the random walk. Second, it is shown that the same accuracy can be obtained also for a simplified algorithm, SLAMS, which is based on averages and requires much less computations.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2001. , 16 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2360
Recursive methods, Accelerated convergence
IdentifiersURN: urn:nbn:se:liu:diva-55805ISRN: LiTH-ISY-R-2360OAI: oai:DiVA.org:liu-55805DiVA: diva2:316526