Accuracy analysis of bias-eliminating least squares estimates for errors-in-variables systems

DOI10.1016/j.automatica.2007.02.002zbMath1128.93393OpenAlexW2046615771MaRDI QIDQ2466917

Mei Hong, Torsten Söderström, Wei Xing Zheng

Publication date: 16 January 2008

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2007.02.002

zbMATH Keywords

system identificationparameter estimationaccuracyerrors-in-variablesbias-eliminating least squares

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Least squares and related methods for stochastic control systems (93E24) Identification in stochastic control theory (93E12)

Related Items (12)

LPV system identification under noise corrupted scheduling and output signal observations ⋮ Accuracy analysis of a covariance matching approach for identifying errors-in-variables systems ⋮ Influence of geometrical distribution of common points on the accuracy of coordinate transformation ⋮ A generalized instrumental variable estimation method for errors-in-variables identification problems ⋮ A bias-corrected estimator for nonlinear systems with output-error type model structures ⋮ A bias-correction method for closed-loop identification of linear parameter-varying systems ⋮ Unifying some higher-order statistic-based methods for errors-in-variables model identification ⋮ Parameter consistency and quadratically constrained errors-in-variables least-squares identification ⋮ A graph subspace approach to system identification based on errors-in-variables system models ⋮ Auxiliary model method for transfer function estimation from noisy input and output data ⋮ Identification of regularized models in the linear regression class ⋮ Identification of EIV models with coloured input–output noise: combining PEM and covariance matching method

Cites Work

• Unnamed Item
• Identification of stochastic linear systems in presence of input noise
• Identification of linear systems with input and output noise: the Koopmans-Levin method
• Unbiased parameter estimation of linear systems in the presence of input and output noise
• Bias correction in least-squares identification
• Transfer function estimation from noisy input and output data
• Frequency domain system identification with missing data
• Stochastic system identification with noisy input-output measurements using polyspectra
• Convergence properties of bias-eliminating algorithms for errors-in-variables identification

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