Composite Modeling of Transfer Functions
1995 (engelsk)Inngår i: Proceedings of the 34th IEEE Conference on Decision and Control, 1995, s. 228-233Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]
The problem under consideration is how to estimate the frequency function of a system and the associated estimation error when a set of possible model structures is given and then one of them is known to contain the true system. The «classical» solution to this problem is to, first, use a consistent model structure selection criterion to discard all but one single structure, second, estimate a model in this structure and, third, conditioned on the assumption that the chosen structure contains the true system, compute an estimate of the estimation error. For a finite data set, however, one cannot guarantee that the correct structure is chosen, and this «structural» uncertainty is lost in the previously mentioned approach. In this contribution a method is developed that combines the frequency function estimates and the estimation errors from all possible structures into a joint estimate and estimation error. Hence, this approach bypasses the structure selection problem. This is accomplished by employing a Bayesian setting. Special attention is given to the choice of priors. With this approach it is possible to benefit from a priori information about the frequency function even though the model structure is unknown.
sted, utgiver, år, opplag, sider
1995. s. 228-233
Emneord [en]
Bayes methods, Identification, Modelling, Probability, Transfer functions
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-93740DOI: 10.1109/CDC.1995.478683ISBN: 0-7803-2685-7 (tryckt)OAI: oai:DiVA.org:liu-93740DiVA, id: diva2:628981
Konferanse
34th IEEE Conference on Decision and Control, New Orleans, LA, USA, December, 1995
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