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Numrering	Referens	Omslagsbild	Hitta
1.	Hjalmarsson, Håkan, 1962-, et al. (författare) Composite modeling of transfer functions 1995 Ingår i: Proceedings of the IEEE Conference on Decision and Control. - New Orleans, LA, USA : Institute of Electrical and Electronics Engineers (IEEE). - 0780326857 ; 40:5, s. 820-832 Konferensbidrag (refereegranskat)abstract 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.

Hjalmarsson, Håkan, 1962-, et al. (författare)
Composite modeling of transfer functions
1995
Ingår i: Proceedings of the IEEE Conference on Decision and Control. - New Orleans, LA, USA : Institute of Electrical and Electronics Engineers (IEEE). - 0780326857 ; 40:5, s. 820-832
Konferensbidrag (refereegranskat)abstract
- 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.

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