Bootstrap-based model uncertainty assessment in continuous-time subspace model identification

Author

Bergamasco, Marco ; Lovera, Marco ; Ohta, Yoshichika

Author_Institution

Dipt. di Elettron., Inf. e Bioingegneria, Politec. di Milano, Milan, Italy

fYear

2013

fDate

10-13 Dec. 2013

Firstpage

5840

Lastpage

5845

Abstract

This paper deals with the problem of model identification in continuous-time using subspace techniques. More precisely, a recently presented continuous-time predictor-based subspace identification algorithm which relies on a system transformation using the Laguerre basis is considered and a bootstrap-based approach to the problem of quantifying the variance error associated with the identified models is proposed.

Keywords

MIMO systems; continuous time systems; discrete time systems; estimation theory; identification; state-space methods; statistical analysis; uncertain systems; Laguerre basis; MIMO systems; SMI; bootstrap-based model uncertainty assessment; continuous-time predictor-based subspace identification algorithm; continuous-time subspace model identification; discrete-time state space model estimation; system transformation; variance error; Computational modeling; Eigenvalues and eigenfunctions; Estimation; Frequency response; Monte Carlo methods; Standards; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on

Conference_Location

Firenze

ISSN

0743-1546

Print_ISBN

978-1-4673-5714-2

Type

conf

DOI

10.1109/CDC.2013.6760810

Filename

6760810