Identification of dynamic errors-in-variables models: Approaches based on two-dimensional ARMA modeling of the data

DOI10.1016/S0005-1098(03)00033-5zbMath1029.93059OpenAlexW2147845541MaRDI QIDQ1400332

Torsten Söderström, Umberto Soverini, Kaushik Mahata

Publication date: 13 August 2003

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0005-1098(03)00033-5

zbMATH Keywords

System identificationFrisch schemeARMA modelErrors-in-variables modelSpectrum estimation

Mathematics Subject Classification ID

Identification in stochastic control theory (93E12)

Related Items (5)

Recursive identification for multivariate errors-in-variables systems ⋮ Identification of errors-in-variables systems with ARMA observation noises ⋮ Autoregressive Process with Measurement Errors ⋮ On the identifiability of errors-in-variables models with white measurement errors ⋮ Errors-in-variables methods in system identification

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• On covariance function tests used in system identification
• The Frisch scheme in dynamic system identification
• Uniquely identifiable state-space and ARMA parametrizations for multivariable linear systems
• Linear identification of ARMA processes
• The Cramér--Rao lower bound for noisy input--output systems.
• Perspectives on errors-in-variables estimation for dynamic systems
• EFFICIENT ESTIMATION OF PARAMETERS IN MOVING-AVERAGE MODELS
• IDENTIFIABILITY IN DYNAMIC ERRORS-IN-VARIABLES MODELS
• Stochastic system identification with noisy input using cumulant statistics
• Frequency domain system identification with missing data

This page was built for publication: Identification of dynamic errors-in-variables models: Approaches based on two-dimensional ARMA modeling of the data