Use Hausdorff metric to analyze convergence of parameter estimation in system identification

DOI10.1016/j.automatica.2014.06.005zbMath1297.93159OpenAlexW2046955518MaRDI QIDQ466308

Zhibin Yan, Caiyun Chen

Publication date: 24 October 2014

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2014.06.005

zbMATH Keywords

system identificationHausdorff metricprediction error methodparameter estimate

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12)

Cites Work

• Assessing the quality of identified models through the asymptotic theory -- when is the result reliable?
• On the uniqueness of maximum likelihood identification
• From experiment design to closed-loop control
• Necessary and sufficient conditions for uniqueness of the minimum in Prediction Error Identification
• Optimal experimental design and some related control problems
• Identification for control: from the early achievements to the revival of experiment design
• Closed-loop identification of MIMO systems: a new look at identifiability and experiment design
• Identification and the Information Matrix: How to Get Just Sufficiently Rich?
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