Composite Modeling of Transfer Functions
1995 (English)In: Proceedings of the 34th IEEE Conference on Decision and Control, 1995, 228-233 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
1995. 228-233 p.
Bayes methods, Identification, Modelling, Probability, Transfer functions
IdentifiersURN: urn:nbn:se:liu:diva-93740DOI: 10.1109/CDC.1995.478683ISBN: 0-7803-2685-7OAI: oai:DiVA.org:liu-93740DiVA: diva2:628981
34th IEEE Conference on Decision and Control, New Orleans, LA, USA, December, 1995