Linear and nonlinear system identification using separable least-squares

DOI10.1016/S0947-3580(99)70146-9zbMath0951.93020MaRDI QIDQ1306057

J. Bruls, B. R. J. Haverkamp, Chun Tung Chou, Verhaegen, Michel

Publication date: 19 December 2000

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

zbMATH Keywords

system identificationnonlinear systemleast squares

Mathematics Subject Classification ID

System identification (93B30) Nonlinear systems in control theory (93C10) Least squares and related methods for stochastic control systems (93E24)

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Uses Software

• SMI Toolbox

Cites Work

• Unnamed Item
• Identifying MIMO Wiener systems using subspace model identification methods
• Constructive algebra methods for the \(L_ 2\)-problem for stable linear systems
• On covariance function tests used in system identification
• Identification and rational \(L^ 2\) approximation: A gradient algorithm
• A new algorithm for \(L_ 2\) optimal model reduction
• Adaptive control of Wiener type nonlinear systems
• 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
• Overlapping block-balanced canonical forms for various classes of linear systems
• Global optimization in action. Continuous and Lipschitz optimization: algorithms, implementations and applications
• Subspace-based methods for the identification of linear time-invariant systems
• Overlapping Block-Balanced Canonical Forms and Parametrizations: The Stable SISO Case
• Fading memory and the problem of approximating nonlinear operators with Volterra series
• Algorithms for Separable Nonlinear Least Squares Problems
• Subspace model identification Part 1. The output-error state-space model identification class of algorithms
• A variable projection method for solving separable nonlinear least squares problems
• Convergence analysis of recursive identification algorithms based on the nonlinear Wiener model
• System identification using balanced parametrizations
• The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems Whose Variables Separate