Identifying MIMO Hammerstein systems in the context of subspace model identification methods
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Publication:4873112
DOI10.1080/00207179608921846zbMath0848.93014OpenAlexW4376561351WikidataQ126245611 ScholiaQ126245611MaRDI QIDQ4873112
Verhaegen, Michel, David T. Westwick
Publication date: 4 June 1996
Published in: International Journal of Control (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207179608921846
Related Items (35)
Subspace identification of multivariable linear parameter-varying systems ⋮ Reconfigurable control of Hammerstein systems after actuator failures: stability, tracking, and performance ⋮ Generation of enhanced initial estimates for Hammerstein systems ⋮ Linear approximation and identification of MIMO Wiener-Hammerstein systems ⋮ Initial estimates for the dynamics of a Hammerstein system ⋮ Bounded error identification of Hammerstein systems through sparse polynomial optimization ⋮ Subspace algorithms for the identification of multivariable dynamic errors-in-variables models ⋮ Modelling and control of Hammerstein system using B-spline approximation and the inverse of De Boor algorithm ⋮ A nonparametric kernel-based approach to Hammerstein system identification ⋮ Identification of block-oriented nonlinear systems starting from linear approximations: a survey ⋮ Identification of Wiener, Hammerstein, and NARX systems as Markov chains with improved estimates for their nonlinearities ⋮ A novel APSO-aided weighted LSSVM method for nonlinear Hammerstein system identification ⋮ Initializing Wiener-Hammerstein models based on partitioning of the best linear approximation ⋮ Markov chain approach to identifying Wiener systems ⋮ Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification ⋮ Subspace identification methods for Hammerstein systems: rank constraint and dimension problem ⋮ Subspace identification for non-linear systems with measured-input non-linearities ⋮ Identification of MIMO Hammerstein models using least squares support vector machines ⋮ A new method for fault prediction of model-unknown nonlinear system ⋮ On the behavior of autonomous Wiener systems ⋮ A model-based PID controller for Hammerstein systems using B-spline neural networks ⋮ Subspace identification of Hammerstein model with unified discontinuous nonlinearity ⋮ Subspace-based identification algorithms for Hammerstein and Wiener models ⋮ Identification for Wiener systems with RTF subsystems ⋮ Extended stochastic gradient identification method for Hammerstein model based on approximate least absolute deviation ⋮ Nonlinear model predictive control based on piecewise linear Hammerstein models ⋮ Identification of Hammerstein systems without explicit parameterisation of non-linearity ⋮ New identification method for Hammerstein models based on approximate least absolute deviation ⋮ Strong consistence of recursive identification for Wiener systems ⋮ Subspace identification of Hammerstein-type nonlinear systems subject to unknown periodic disturbance ⋮ System identification of Wiener systems with B-spline functions using De Boor recursion ⋮ Identification of Hammerstein systems with continuous nonlinearity ⋮ Discussion on: ``Subspace-based identification algorithms for Hammerstein and Wiener models ⋮ Discussion on: ``Subspace-based identification algorithms for Hammerstein and Wiener models ⋮ Discussion on: ``Subspace-based identification algorithms for Hammerstein and Wiener models
Uses Software
Cites Work
- The identification of nonlinear biological systems: LNL cascade models
- Application of a subspace model identification technique to identify LTI systems operating in closed-loop
- Structure identification of nonlinear dynamic systems - A survey on input/output approaches
- Identification of discrete Hammerstein systems using kernel regression estimates
- Instrumental-variable methods for identification of Hammerstein systems
- Subspace model identification Part 1. The output-error state-space model identification class of algorithms
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