Dynamic GP models: an overview and recent developments

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 probabilistic, nonparametric model with uncertainty predictions. It possesses several interesting features like model predictions contain the measure of confidence. Further, the model has a small number of training parameters, a facilitated structure determination and different possibilities of including prior knowledge about the modelled system. The framework for the identification of dynamic systems with GP models are presented and an overview of recent advances in the research of dynamic-system identification with GP models and its applications are given. Key–Words: Nonlinear-system identification, Gaussian process models, dynamic systems, regression, control systems, fault detection, Bayesian filtering.