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DOKOUPIL, J. VÁCLAVEK, P.
Originální název
Regularized estimation with variable exponential forgetting
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
The real-time estimation of normal regression-type models with unknown time-varying parameters is considered and discussed from the Bayesian perspective. A novel tracking technique combining the variable regularization approach with the forgetting operation is derived and elaborated into algorithmic details. The regularization of the parameter covariance is accomplished by incorporating soft equality constraints on the regression parameters into the learning procedure. The resultant procedure is designed to smooth the parameter estimate, preventing it from changing too rapidly. Moreover, the form of the constraints guarantees a minimal amount of information about the parameter estimate, which makes the estimator robust with respect to poor system excitation. The forgetting of obsolete information is provided in two different parameterization options and is performed automatically in a way that complies with the degree of the process nonstationarity. The whole concept preserves the selfreproducibility of the statistics of the normal-Wishart distribution.
Klíčová slova
forgetting factor; Kullback-Leibler divergence; normal-Wishart distribution
Autoři
DOKOUPIL, J.; VÁCLAVEK, P.
Vydáno
14. 12. 2020
Nakladatel
IEEE
Místo
New York
ISBN
978-1-7281-7446-4
Kniha
59th Conference on Decision and Control
Strany od
312
Strany do
318
Strany počet
7
URL
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9304385
BibTex
@inproceedings{BUT167334, author="Jakub {Dokoupil} and Pavel {Václavek}", title="Regularized estimation with variable exponential forgetting", booktitle="59th Conference on Decision and Control", year="2020", pages="312--318", publisher="IEEE", address="New York", doi="10.1109/CDC42340.2020.9304385", isbn="978-1-7281-7446-4", url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9304385" }