On estimation of errors caused by non-linear undermodelling in system identification

DOI10.1080/00207170210159113zbMath1024.93018OpenAlexW2094257535MaRDI QIDQ4803172

Torbjörn Wigren, Anders E. Nordsjö

Publication date: 15 July 2003

Published in: International Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207170210159113

zbMATH Keywords

identificationerror estimationnonlinearity undermodellingstochastic embedding approach

Mathematics Subject Classification ID

System identification (93B30) Nonlinear systems in control theory (93C10)

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Cites Work

• Unnamed Item
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• Identification and application of bounded-parameter models
• A class of algorithms for identification in \(\mathcal H_{\infty}\)
• The kernel identification method (1910-1977)-review of theory, calculation, application, and interpretation
• Recusrsive prediction error identification using the nonlinear Wiener model
• Estimation of model quality
• A stochastic embedding approach for quantifying uncertainty in the estimation of restricted complexity models
• Estimation theory and uncertainty intervals evaluation in presence of unknown but bounded errors: Linear families of models and estimators
• Quantifying the error in estimated transfer functions with application to model order selection
• Error and Perturbation Bounds for Subspaces Associated with Certain Eigenvalue Problems
• Convergence analysis of recursive identification algorithms based on the nonlinear Wiener model
• Circle criteria in recursive identification
• Cramer-Rao bounds for a class of systems described by Wiener and Hammerstein models
• Approximate gradients, convergence and positive realness in recursive identification of a class of non‐linear systems
• Asymptotic Theory for Principal Component Analysis

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