INCORPORATING LINEAR LOCAL MODELS IN GAUSSIAN PROCESS MODEL

Identification of nonlinear dynamic systems from experimental data can be difficult when, as often happens, more data are available around equilibrium points and only sparse data are available far from those points. The probabilistic Gaussian Process model has already proved to model such systems efficiently. The purpose of this paper is to show how one can relatively easily combine measured data and linear local models in this model. Also, using previous results, we show how uncertainty can be propagated through such models when predicting ahead in time in an iterative manner. The approach is illustrated with a simple numerical example.