Consistency and asymptotic normality of some subspace algorithms for systems without observed inputs
type: Item
instance of: scholarly article
title: Consistency and asymptotic normality of some subspace algorithms for systems without observed inputs
zbMATH Open document ID: 0944.93008
DOI: 10.1016/S0005-1098(99)00031-X
published in: Automatica
publication date: 1999-09-02T00:00:00Z
review text: For a class of subspace algorithms, in the case where no observed inputs are included, the asymptotic properties of the estimates of the system matrices are discussed. One proves the asymptotic normality of subspace estimates and that the variance may be calculated. A new consistency result for the system matrix is presented.
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 93E12
zbMATH DE Number: 1331898
zbMATH Keywords: identification
zbMATH Keywords: asymptotic analysis
zbMATH Keywords: discrete time systems
zbMATH Keywords: linear multivariable systems
zbMATH Keywords: subspace algorithms
zbMATH Keywords: asymptotic normality
zbMATH Keywords: consistency
author: Dietmar Bauer
author: Manfred Deistler
author: Wolfgang Scherrer
reviewed by: Mihail Voicu
MaRDI profile type: Publication
full work available at URL: s0005-1098(99)00031-x
OpenAlex ID: W2030181817
description: scientific article; zbMATH DE number 1331898