A covariance matching approach for identifying errors-in-variables systems

DOI10.1016/j.automatica.2009.05.010zbMath1175.93231OpenAlexW2029349088MaRDI QIDQ1036667

Torsten Söderström, Magnus Mossberg, Mei Hong

Publication date: 13 November 2009

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2009.05.010

zbMATH Keywords

system identificationlinear systemserrors-in-variables modelscovariance functions

Mathematics Subject Classification ID

Linear systems in control theory (93C05) Least squares and related methods for stochastic control systems (93E24) Identification in stochastic control theory (93E12) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (12)

Errors-in-variables identification using maximum likelihood estimation in the frequency domain ⋮ The Frisch scheme in multivariable errors-in-variables identification ⋮ A novel two-stage estimation algorithm for nonlinear Hammerstein-Wiener systems from noisy input and output data ⋮ A confirmatory factor analysis approach for addressing the errors-in-variables problem with colored output noise ⋮ Accuracy analysis of a covariance matching approach for identifying errors-in-variables systems ⋮ Novel parameter estimation of autoregressive signals in the presence of noise ⋮ A generalized instrumental variable estimation method for errors-in-variables identification problems ⋮ Extended accuracy analysis of a covariance matching approach for identifying errors-in-variables systems ⋮ Approximative weighting for a covariance-matching approach for identifying errors-in-variables systems ⋮ On the identifiability of errors-in-variables models with white measurement errors ⋮ Non-asymptotic confidence regions for the parameters of EIV systems ⋮ Errors-in-variables system identification using structural equation modeling

Cites Work

• On covariance function tests used in system identification
• The Frisch scheme in dynamic system identification
• Relations between bias-eliminating least squares, the Frisch scheme and extended compensated least squares methods for identifying errors-in-variables systems
• Hierarchical gradient-based identification of multivariable discrete-time systems
• Extending the Frisch scheme for errors-in-variables identification to correlated output noise
• On the convergence of pseudo-linear regression algorithms
• Transfer function estimation from noisy input and output data
• Separable nonlinear least squares: the variable projection method and its applications
• Performance analysis of estimation algorithms of nonstationary ARMA processes
• A bias correction method for identification of linear dynamic errors-in-variables models
• Hierarchical least squares identification methods for multivariable systems
• The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems Whose Variables Separate
• Errors-in-variables methods in system identification
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: A covariance matching approach for identifying errors-in-variables systems