Sciweavers

AUTOMATICA
2004

Non-asymptotic quality assessment of generalised FIR models with periodic inputs

13 years 3 months ago
Non-asymptotic quality assessment of generalised FIR models with periodic inputs
In any real-life identification problem, only a finite number of data points is available. On the other hand, almost all results in stochastic identification pertain to asymptotic properties, that is they tell us what happens when the number of data points tends to infinity. In this paper we consider the problem of assessing the quality of the estimates identified from a finite number of data points. We focus on least squares identification of generalised FIR models and develop a method to produce a bound on the uncertainty in the parameter estimate. The method is data driven and based on tests involving permuted data sets. Moreover, it does not require that the true system is in the model class. 2004 Elsevier Ltd. All rights reserved.
Marco C. Campi, Su Ki Ooi, Erik Weyer
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2004
Where AUTOMATICA
Authors Marco C. Campi, Su Ki Ooi, Erik Weyer
Comments (0)