The Gaussian process (GP) model has been applied to the identification of a process model.
The GP model can be represented by its mean and covariance function. It provides …

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 …

Gaussian process models provide a probabilistic non-parametric modelling approach for
black-box identification of non-linear dynamic systems. The Gaussian processes can …

Nonlinearity of process systems along with colored noises is common in chemical
processes. A multivariate (multiple inputs and multiple outputs) Gaussian process …

The traditional Gaussian Process model is not analytically invertible. In order to use the
Gaussian Process model for Internal Model Control, numerical approaches have to be used …

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 …

In traditional system identification methods, it is often assumed that the output data are
corrupted by Gaussian white noise which is independent and identically distributed (iid) …

With its added features in the measurement of confidence, and lower demands on the
training parameters, the Gaussian process regression (GPR) model has been shown to be a …

This paper introduces a Gaussian process regression (GPR) model which could adapt to
both linear and nonlinear systems automatically without prior introduction of kernel …

Gaussian Process (GP), as a probabilistic non-linear multi-variable regression model, has
been widely used in nonparametric Bayesian framework for the data based modelling of …