scientific article

zbMath1177.93089MaRDI QIDQ3639845

Roberto Diversi, Umberto Soverini, Roberto P. Guidorzi

Publication date: 26 October 2009

Full work available at URL: https://eudml.org/doc/33952

Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.

zbMATH Keywords

system identificationlinear systemsFrisch schemeerrors-in-variables models

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Linear systems in control theory (93C05) Identification in stochastic control theory (93E12) Positive matrices and their generalizations; cones of matrices (15B48)

Related Items (18)

Frisch Scheme Identification of Robots Dynamic Parameters ⋮ Maximum likelihood identification of noisy input-output models ⋮ Identification of multivariable dynamic errors-in-variables system with arbitrary inputs ⋮ Errors-in-variables identification using maximum likelihood estimation in the frequency domain ⋮ The Frisch scheme in multivariable errors-in-variables identification ⋮ Structural monitoring of a tower by means of MEMS-based sensing and enhanced autoregressive models ⋮ Direction–of–Arrival Estimation in Nonuniform Noise Fields: A Frisch Scheme Approach ⋮ A confirmatory factor analysis approach for addressing the errors-in-variables problem with colored output noise ⋮ Accuracy analysis of a covariance matching approach for identifying errors-in-variables systems ⋮ A generalized instrumental variable estimation method for errors-in-variables identification problems ⋮ Extended accuracy analysis of a covariance matching approach for identifying errors-in-variables systems ⋮ Approximative weighting for a covariance-matching approach for identifying errors-in-variables systems ⋮ A generalised instrumental variable estimator for multivariable errors-in-variables identification problems ⋮ Frequency domain identification of FIR models in the presence of additive input-output noise ⋮ A covariance matching approach for identifying errors-in-variables systems ⋮ A unified framework for EIV identification methods when the measurement noises are mutually correlated ⋮ Bias-eliminating least-squares identification of errors-in-variables models with mutually correlated noises ⋮ Errors-in-variables system identification using structural equation modeling

Cites Work

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