Comparing some classes of bias-compensating least squares methods

DOI10.1016/J.AUTOMATICA.2013.01.003zbMath1267.93176OpenAlexW2086893659MaRDI QIDQ357574

Torsten Söderström

Publication date: 30 July 2013

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: http://www.sciencedirect.com/science/article/pii/S0005109813000046

zbMATH Keywords

system identificationlinear systemserrors-in-variables modelsbias compensation

Mathematics Subject Classification ID

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

Related Items (5)

Parameter identification of a class of nonlinear systems based on the multi-innovation identification theory ⋮ A novel two-stage estimation algorithm for nonlinear Hammerstein-Wiener systems from noisy input and output data ⋮ Novel parameter estimation of autoregressive signals in the presence of noise ⋮ Bias compensation principle based recursive least squares identification method for Hammerstein nonlinear systems ⋮ Least-squares based and gradient based iterative parameter estimation algorithms for a class of linear-in-parameters multiple-input single-output output error systems

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