Analysis of the asymptotic properties of the MOESP type of subspace algorithms
type: Item
instance of: scholarly article
title: Analysis of the asymptotic properties of the MOESP type of subspace algorithms
zbMATH Open document ID: 0984.93014
DOI: 10.1016/S0005-1098(99)00174-0
author: Yanyan Li
published in: Automatica
publication date: 2002-05-05T00:00:00Z
review text: Subspace algorithms are used for the estimation of linear, time-invariant, finite-dimensional, discrete-time, state-space systems. The paper uses the MOESP type of subspace algorithms. They are based on the geometric structure of covariance matrices and exploit the properties of the state vector extensively. The asymptotic properties of the algorithms are examined. The main results concern consistency and asymptotic normality for the estimates of the system matrices under suitable assumptions. Numerical examples are given, and confidence regions for estimates of different system related quantities can be computed using this asymptotic theory.
Mathematics Subject Classification ID: 93B30
zbMATH DE Number: 1437545
zbMATH Keywords: identification
zbMATH Keywords: subspace algorithms
zbMATH Keywords: MOESP type algorithms
zbMATH Keywords: covariance matrices
zbMATH Keywords: asymptotic properties
zbMATH Keywords: consistency
zbMATH Keywords: asymptotic normality
Wikidata QID: Q62586024
reviewed by: Yves Cherruault
MaRDI profile type: Publication
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DBLP publication ID: journals/automatica/BauerJ00
description: scientific article; zbMATH DE number 1437545