A new method for the identification of Hammerstein model

DOI10.1016/S0005-1098(97)00105-2zbMath0894.93012MaRDI QIDQ1376277

M. Nazmul Karim, H. Al-Duwaish

Publication date: 17 December 1997

Published in: Automatica (Search for Journal in Brave)

zbMATH Keywords

identificationneural networkrecursive least squaresHammerstein systemsbackpropagationparametric ARMA model

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) System identification (93B30) Nonlinear systems in control theory (93C10) General systems theory (93A99)

Related Items (6)

Recursive identification of continuous-time Hammerstein systems ⋮ Optimal experiment design for regression polynomial models identification ⋮ A novel APSO-aided maximum likelihood identification method for Hammerstein systems ⋮ Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification ⋮ A nonlinear recursive instrumental variables identification method of Hammerstein ARMAX system ⋮ Neural network approach for identification of Hammerstein systems

Cites Work

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• The identification of nonlinear biological systems: Wiener and Hammerstein Cascade models
• Discrete time adaptive control of linear dynamic systems with a two-segment piecewise-linear asymmetric nonlinearity
• On the design of nonlinear discrete-time predictors (Corresp.)
• Identification of discrete Hammerstein systems using kernel regression estimates
• Nonparametric identification of Hammerstein systems
• A comparison of two Hammerstein model identification algorithms
• An iterative method for the identification of nonlinear systems using a Uryson model
• Detectors for discrete-time signals in non-Gaussian noise

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