Composite modeling of transfer functions
1995 (English)In: Proceedings of the IEEE Conference on Decision and Control, New Orleans, LA, USA, 1995, Vol. 1, no Piscataway, NJ, United States, 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 when one of them is known to contain the true system. The 'classical' solution to this problem is to, firstly, use a consistent model structure selection criterium to discard all but one single structure. Secondly, estimate a model in this structure and, thirdly, conditioned on the assumption that the chosen structure contains the true system, compute an estimate of the estimation error. However, for a finite data set 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 by-passes the structure selection problem. This is accomplished by employing a Bayesian setting.
Place, publisher, year, edition, pages
New Orleans, LA, USA, 1995. Vol. 1, no Piscataway, NJ, United States, 228-233 p.
, Proceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4)
Data structures, Errors, Estimation, Frequency domain analysis, Identification (control systems), Mathematical models, Regression analysis, Bayesian setting, Composite modeling, Estimation error, Finite data set, Frequency function, Transfer functions
Research subject SRA - ICT
IdentifiersURN: urn:nbn:se:kth:diva-60590DOI: 10.1109/CDC.1995.478683OAI: oai:DiVA.org:kth-60590DiVA: diva2:477535
Sponsors: IEEE NR 201408052012-01-132012-01-132012-01-13Bibliographically approved