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

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 …

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 …

Gaussian process (GP) models are nowadays considered among the state-of-the-art tools in
modern dynamic system identification. GP models are probabilistic, non-parametric models …

Gaussian processes provide an approach to nonparametric modelling which allows a
straightforward combination of function and derivative observations in an empirical model …

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 processes are used in machine learning to learn input-output mappings from
observed data. Gaussian process regression is based on imposing a Gaussian process …

By physical (theoretical) modeling of dynamic systems, one usually obtains the structure as
well as the parameters of the mathematical model. The model parameters can generally be …

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 …