Bias-eliminating least-squares identification of errors-in-variables models with mutually correlated noises

DOI10.1002/acs.2365zbMath1278.93280OpenAlexW1930961411MaRDI QIDQ2868151

Roberto Diversi

Publication date: 23 December 2013

Published in: International Journal of Adaptive Control and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/acs.2365

zbMATH Keywords

system identificationerrors-in-variables modelsbias-eliminating least squaresmutually correlated white noises

Mathematics Subject Classification ID

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

Related Items (7)

Recursive identification for dynamic linear systems from noisy input-output measurements ⋮ Identification of multivariable dynamic errors-in-variables system with arbitrary inputs ⋮ Identification of dual‐rate sampled errors‐in‐variables systems with time delays ⋮ The data-filtering based bias compensation recursive least squares identification for multi-input single-output systems with colored noises ⋮ Recursive identification of errors-in-variables systems based on the correlation analysis ⋮ The bias compensation based parameter and state estimation for observability canonical state-space models with colored noise ⋮ A unified framework for EIV identification methods when the measurement noises are mutually correlated

Cites Work

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• Bias correction in least-squares identification
• Transfer function estimation from noisy input and output 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
• Errors-in-variables methods in system identification

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