This thesis is concerned with investigating the use of Gaussian Process (GP) models for the
identification of nonlinear dynamic systems. The Gaussian Process model is a non …

This paper describes the identification of nonlinear dynamic systems with a Gaussian
process (GP) prior model. This model is an example of the use of a probabilistic non …

Various methods can be used for nonlinear, dynamic-system identification and Gaussian
process (GP) model is a relatively recent one. The GP model is an example of a …

The paper describes the identification of nonlinear dynamic systems with a Gaussian
process prior model. This approach is an example of a probabilistic, non-parametric …

Gaussian process (GP) models form an emerging methodology for modelling nonlinear
dynamic systems which tries to overcome certain limitations inherent to traditional methods …

In the past years many approaches to modelling of nonlinear systems using neural networks
and fuzzy models have been proposed [1–3]. The difficulties associated with these black …

Gaussian process (GP) models form a new, emerging complementary method for nonlinear
system identification. The GP model is a probabilistic nonparametric black-box model. It …

In this paper an alternative approach to black-box identification of non-linear dynamic
systems is compared with the more established approach of using artificial neural networks …

We are living in an era of rapidly developing technology. Dynamic systems control is not a
new methodology, but it is heavily influenced by the development of technologies for …

Abstract System identification comprises a number of linear and nonlinear tools for black-
box modeling of dynamical systems, with applications in several areas of engineering …