Least-squares estimation of a class of frequency functions: a finite sample variance expression

DOI10.1016/j.automatica.2005.12.021zbMath1102.93012OpenAlexW1973493299MaRDI QIDQ856503

Håkan Hjalmarsson, Brett Ninness

Publication date: 7 December 2006

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2005.12.021

zbMATH Keywords

FIR modelsfrequency functionperiodic signalsLaguerre modelsKautz modelsmodel variance

Mathematics Subject Classification ID

System identification (93B30) Design techniques (robust design, computer-aided design, etc.) (93B51) Stochastic systems in control theory (general) (93E03)

Related Items (10)

Convergence analysis of EFOP estimate based on frequency domain smoothing ⋮ Accuracy of linear multiple-input multiple-output (MIMO) models obtained by maximum likelihood estimation ⋮ Non-asymptotic model quality assessment of transfer functions at multiple frequency points ⋮ On input design for regularized LTI system identification: power-constrained input ⋮ Variance analysis of identified linear MISO models having spatially correlated inputs, with application to parallel Hammerstein models ⋮ Variance analysis of linear SIMO models with spatially correlated noise ⋮ Covariance analysis in SISO linear systems identification ⋮ Non-asymptotic confidence regions for model parameters in the presence of unmodelled dynamics ⋮ Variance-error quantification for identified poles and zeros ⋮ On embeddings and inverse embeddings of input design for regularized system identification

Cites Work

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• System identification using Kautz models
• Identification For Control: Optimal Input Design With Respect To A Worst-Case $\nu$-gap Cost Function
• The fundamental role of general orthonormal bases in system identification
• Asymptotic variance expressions for estimated frequency functions
• The Effect of Regularization on Variance Error
• Variance Error Quantifications That Are Exact for Finite-Model Order
• Input design via LMIs admitting frequency-wise model specifications in confidence regions
• Control-relevant experiment design for multivariable systems described by expansions in orthonormal bases

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