Asymptotically Optimal Smoothing of Averaged LMS for Regression Parameter Tracking
2002 (English)In: Proceedings of the 15th IFAC Congress, 2002, 436-436 p.Conference paper (Refereed)
The sequence of estimates formed by the LMS algorithm for a standard linear regression estimation problem is considered. It is known since earlier that smoothing these estimates by simple averaging will lead to, asymptotically, the recursive least squares algorithm. In thi spaper 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 slowly changing as a random walk. The choice of smoothing matrix should be tailored to theproperties of the random walk. Second, it is shown that the same accuracy can be obtained also for a modified algorithm, SLAMS, which is based on averages and requires much less computations.
Place, publisher, year, edition, pages
2002. 436-436 p.
Regression, Parameter estimation, Random walk, Recursive algorithms, Tracking, Smoothing, Mean-square error, Asymptotic properties
Engineering and Technology Control Engineering
IdentifiersURN: urn:nbn:se:liu:diva-90842DOI: 10.3182/20020721-6-ES-1901.00438ISBN: 978-3-902661-74-6OAI: oai:DiVA.org:liu-90842DiVA: diva2:615794
15th IFAC Congress, Barcelona, Spain, July, 2002
FunderSwedish Research Council