What can regularization offer for estimation of dynamical systems?
2013 (English)In: 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP13, IFAC , 2013, 1-8 p.Conference paper (Refereed)
Estimation of unknown dynamics is what system identication is about and acore problem in adaptive control and adaptive signal processing. It has long been known thatregularization can be quite benecial for general inverse problems of which system identicationis an example. But only recently, partly under the inuence of machine learning, the use ofwell tuned regularization for estimating linear dynamical systems has been investigated moreseriously. In this presentation we review these new results and discuss what they may mean forthe theory and practice of dynamical model estimation in general.
Place, publisher, year, edition, pages
IFAC , 2013. 1-8 p.
Identification methods design and analysis, Bayesian learning, Linear system identification
IdentifiersURN: urn:nbn:se:liu:diva-96766DOI: 10.3182/20130703-3-FR-4038.00155ISBN: 978-390282337-3OAI: oai:DiVA.org:liu-96766DiVA: diva2:643258
11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing (ALCOSP13), 3-5 July 2013, Caen, France