An identification methodology for nonlinear dynamic systems using Gaussian process prior models is presented. It combines equilibrium and off-equilibrium information. The advantages of so doing are highlighted
Identification of nonlinear systems is a problem with many facets and roots in several diverse field...
Identification of nonlinear systems is a problem with many facets and roots in several diverse field...
The problem of nonlinear dynamical systems of Wiener type identification is considered. The linear...
An identification methodology for nonlinear dynamic systems using Gaussian process prior models is p...
An identification methodology for nonlinear dynamic systems using Gaussian process prior models is p...
This paper describes the identification of nonlinear dynamic systems with a Gaussian process (GP) pr...
We investigate the reconstruction of nonlinear systems from locally identified linear models. It is ...
We investigate the reconstruction of nonlinear systems from locally identified linear models. It is ...
Abstract: In the note several algorithms for nonlinear system identification are presented. The clas...
We investigate the reconstruction of nonlinear systems from locally identified linear models. It is ...
Abstract — Different models can be used for nonlinear dy-namic systems identification and the Gaussi...
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward com...
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward com...
Gaussian process prior models are known to be a powerful non-parametric tool for stochastic data mod...
This book provides engineers and scientists in academia and industry with a thorough understanding o...
Identification of nonlinear systems is a problem with many facets and roots in several diverse field...
Identification of nonlinear systems is a problem with many facets and roots in several diverse field...
The problem of nonlinear dynamical systems of Wiener type identification is considered. The linear...
An identification methodology for nonlinear dynamic systems using Gaussian process prior models is p...
An identification methodology for nonlinear dynamic systems using Gaussian process prior models is p...
This paper describes the identification of nonlinear dynamic systems with a Gaussian process (GP) pr...
We investigate the reconstruction of nonlinear systems from locally identified linear models. It is ...
We investigate the reconstruction of nonlinear systems from locally identified linear models. It is ...
Abstract: In the note several algorithms for nonlinear system identification are presented. The clas...
We investigate the reconstruction of nonlinear systems from locally identified linear models. It is ...
Abstract — Different models can be used for nonlinear dy-namic systems identification and the Gaussi...
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward com...
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward com...
Gaussian process prior models are known to be a powerful non-parametric tool for stochastic data mod...
This book provides engineers and scientists in academia and industry with a thorough understanding o...
Identification of nonlinear systems is a problem with many facets and roots in several diverse field...
Identification of nonlinear systems is a problem with many facets and roots in several diverse field...
The problem of nonlinear dynamical systems of Wiener type identification is considered. The linear...