Frequency domain maximum likelihood estimation of linear dynamic errors-in-variables models

DOI10.1016/j.automatica.2006.10.004zbMath1261.93077OpenAlexW1974632752MaRDI QIDQ880412

Rik Pintelon, Johan Schoukens

Publication date: 15 May 2007

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2006.10.004

zbMATH Keywords

frequency domainmaximum likelihooderrors-in-variablesdiscrete-time modelscontinuous-time modelsCramér--Rao lower boundGauss--Newton minimization scheme

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Non-Markovian processes: estimation (62M09) Estimation and detection in stochastic control theory (93E10)

Related Items (8)

Discussion on: ``Identification of ARX and ARARX models in the presence of input and output noises ⋮ Identification of dynamic errors-in-variables systems with quasi-stationary input and colored noise ⋮ Errors-in-variables identification using maximum likelihood estimation in the frequency domain ⋮ The Frisch scheme in multivariable errors-in-variables identification ⋮ Improved parameter bounds for set-membership EIV problems ⋮ Unifying some higher-order statistic-based methods for errors-in-variables model identification ⋮ L₂-optimal identification of MIMO errors-in-variables models from the v-gap geometrical interpretation ⋮ Errors-in-variables methods in system identification

Cites Work

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• On covariance function tests used in system identification
• Theory and applications of adaptive control - a survey
• Adaptive computed torque control for rigid link manipulations
• The identification of multivariate linear dynamic errors-in-variables models
• The Cramér--Rao lower bound for noisy input--output systems.
• Identification of dynamic errors-in-variables models
• Identification of continuous-time errors-in-variables models
• Box-Jenkins identification revisited. I: Theory
• Box-Jenkins identification revisited. II: Applications
• IDENTIFIABILITY IN DYNAMIC ERRORS-IN-VARIABLES MODELS
• Identification of invariants of (over)parameterized models: finite sample results
• Frequency domain system identification with missing data
• A bias correction method for identification of linear dynamic errors-in-variables models
• Convergence properties of bias-eliminating algorithms for errors-in-variables identification

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