Frequency-domain subspace system identification using non-parametric noise models
type: Item
instance of: scholarly article
title: Frequency-domain subspace system identification using non-parametric noise models
zbMATH Open document ID: 1008.93066
DOI: 10.1016/S0005-1098(02)00036-5
published in: Automatica
publication date: 2002-09-05T00:00:00Z
review text: The paper is devoted to a study of the stochastic properties of a frequency-domain subspace algorithm using the noise covariance matrix. The true noise covariance matrix is replaced by a sample covariance matrix, obtained from a small number of independent experiments. A basic system model in the frequency-domain subspace is developed. An original system identification algorithm is obtained. It is analyzed with the assumption of an exactly known system input and a proper system. The case of not meeting these assumptions is also discussed. The strong consistency, the strong convergence, the convergence rate, and the asymptotic normality are preserved. In the absence of model errors the uncertainty is slightly increased. Some illustrative real and simulation measurement examples are given.
reviewed by: Tzvetan Semerdjiev
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93C80
Mathematics Subject Classification ID: 93C57
zbMATH DE Number: 1797113
zbMATH Keywords: system identification
zbMATH Keywords: frequency-domain subspace algorithm
zbMATH Keywords: sample covariance matrix
zbMATH Keywords: consistency
zbMATH Keywords: asymptotic normality
author: Rik Pintelon
MaRDI profile type: Publication
cites work: Q3255781
cites work: Q4836494
cites work: Q3947020
cites work: Q5689624
cites work: Fast projection methods for minimal design problems in linear system theory
cites work: Q4070067
cites work: Subspace-based multivariable system identification from frequency response data
cites work: Frequency domain system identification with missing data
cites work: Analyses, Development, and Applications of TLS Algorithms in Frequency Domain System Identification
cites work: A first course in numerical analysis.
cites work: Best conditioned parametric identification of transfer function models in the frequency domain
cites work: Frequency-domain system identification using non-parametric noise models estimated from a small number of data sets
cites work: Q3757131
cites work: Continuous-time frequency domain subspace system identification
cites work: Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
description: scientific article; zbMATH DE number 1797113