An example of Gaussian process model identification

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 modelling. Gaussian process model can be considered as the special case of radial basis function network and as such an alternative to neural networks or fuzzy black box models. An attractive feature of Gaussian process model is that the variance, associated with the model response, is readily obtained. Variance can be seen as uncertainty of the model and can be used to obtain more accurate multi-step ahead prediction. The method is demonstrated on laboratory pilot plant identification.