The identification of nonlinear biological systems: LNL cascade models
Biological Cybernetics
Identification of systems containing linear dynamic and static nonlinear elements
Automatica (Journal of IFAC)
Brief Identification of linear systems with hard input nonlinearities of known structure
Automatica (Journal of IFAC)
Brief Parameter identification of a class of Hammerstein plants
Automatica (Journal of IFAC)
Linear approximations of nonlinear FIR systems for separable input processes
Automatica (Journal of IFAC)
Identification of linear systems with nonlinear distortions
Automatica (Journal of IFAC)
Identification of MIMO Hammerstein models using least squares support vector machines
Automatica (Journal of IFAC)
Identification methods for Hammerstein nonlinear systems
Digital Signal Processing
Automatica (Journal of IFAC)
Hi-index | 22.15 |
This paper studies the generation of initial estimates for the dynamic part of a Hammerstein model. It will be shown that ARMAX or Box-Jenkins models result in better initial estimates than ARX or output-error (OE) models even in the absence of disturbing noise. This will be proven by noticing that a static nonlinear system can be replaced by a static gain plus a nonlinear noise source that acts in a completely similar way to disturbing noise for the study of the second-order properties of the estimators in the prediction error framework.