Title :
Estimating model variance in the case of undermodeling
Author :
Hjalmarsson, Haåkan ; Ljung, Lennart
Author_Institution :
Dept. of Chem. Eng., California Inst. of Technol., Pasadena, CA, USA
fDate :
7/1/1992 12:00:00 AM
Abstract :
A reliable quality estimate of a given model is a prerequisite for any reasonable use of the model. The model error consists of two different contributions: the bias error and the random error. In this contribution, it is shown that the size (variance) of the random error can be reliably estimated in the case where a true system description cannot be achieved in the model structure used. This consistent error estimate can differ considerably from the conventionally used variance estimate, which could thus be misleading
Keywords :
modelling; parameter estimation; bias error; model variance estimation; parameter estimation; quality estimate; random error; undermodeling; Autocorrelation; Computer aided software engineering; Control system synthesis; Mathematical model; Reduced order systems; Robust control; Robustness; Stochastic processes; System identification; Transfer functions;
Journal_Title :
Automatic Control, IEEE Transactions on
