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Recursive Least Squares and Accelerated Convergence in Stochastic Approximation Schemes
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2000 (English)Report (Other academic)
Abstract [en]

The so-called accelerated convergence is an ingenuous idea to improve the asymptotic accuracy in stochastic approximation (gradient based) algorithms. The estimates obtained from the basic algorithm are subjected to a second round of averaging, which leads to optimal accuracy for estimates of time-invariant parameters. In this contribution, some simple calculations are used to get some intuitive insight into these mechanisms. Of particular interest is to investigate the properties of accelerated convergence schemes in tracking situations. It is shown that a second round of averaging leads to the recursive least-squares algorithm with a forgetting factor. This also means that in case the true parameters are changing as a random walk, accelerated convergence does not, typically, give optimal tracking properties.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2000. , 11 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2291
Keyword [en]
Stochastic approximation, Recursive estimation, Recursive least squares
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-55752ISRN: LiTH-ISY-R-2291OAI: diva2:316581
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-09-05Bibliographically approved

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Ljung, Lennart
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Automatic ControlThe Institute of Technology
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