Recursive subspace identification of linear and nonlinear Wiener state-space models

DOI10.1016/S0005-1098(00)00103-5zbMath0980.93015OpenAlexW1985718264MaRDI QIDQ5925917

Gustafsson, Tony, Verhaegen, Michel, Lovera, Marco

Publication date: 5 July 2001

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0005-1098(00)00103-5

zbMATH Keywords

instrumental variableMIMO systemsnonlinear Wiener type modelsrecursive identificationsubspace methods

Mathematics Subject Classification ID

System identification (93B30) Nonlinear systems in control theory (93C10) Multivariable systems, multidimensional control systems (93C35)

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Uses Software

• SMI Toolbox

Cites Work

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• Identifying MIMO Wiener systems using subspace model identification methods
• Recusrsive prediction error identification using the nonlinear Wiener model
• Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
• Asymptotic convergence analysis of the projection approximation subspace tracking algorithms
• Subspace algorithms for the identification of multivariable dynamic errors-in-variables models
• Fast subspace-based system identification: An instrumental variable approach
• Subspace-based methods for the identification of linear time-invariant systems
• Subspace model identification Part 3. Analysis of the ordinary output-error state-space model identification algorithm
• Subspace model identification Part 1. The output-error state-space model identification class of algorithms
• Convergence analysis of recursive identification algorithms based on the nonlinear Wiener model
• Recursive subspace identification of linear and nonlinear Wiener state-space models