On the Use of Regularization in System Identification
1993 (English)In: Proceedings of the 12th IFAC World Congress, 1993, 381-386 p.Conference paper (Refereed)
Regularization is a standard statistical technique to deal with ill-conditioned parameter estimation problems. We discuss in this contribution what possibilities and advantages regularization offers in system identification. In the first place regularization reduces the variance error of a model, but at the same time it introduces a bias. The familiar trade-off between bias and variance error for the choice of model order/structure can therefore be discussed in terms of the regularization parameter. We also show how the well-known problem of parametrizing multivariable system can be dealt with using overparametrization plus regularization. A characteristic feature for this way of letting the parametrization/model structure/model order be solved by regularization is that it is an easy and "automatic" way of finding the important parameters and good parametrization. No statistical penalty is paid for the overparametrization, but there is a penalty of higher computational burden.
Place, publisher, year, edition, pages
1993. 381-386 p.
Regularization, Identification, Parametrization, Overparametrization, Model selection
IdentifiersURN: urn:nbn:se:liu:diva-94092ISBN: 978-0080422121OAI: oai:DiVA.org:liu-94092DiVA: diva2:629715
12th IFAC World Congress, Sydney, Australia, July, 1993