Article ID Journal Published Year Pages File Type
491857 Simulation Modelling Practice and Theory 2008 13 Pages PDF
Abstract

The Gaussian-process (GP) model is an example of a probabilistic, nonparametric model with uncertainty predictions. It can be used for the modelling of complex nonlinear systems and also for dynamic systems identification. The output of the GP model is a normal distribution, expressed in terms of the mean and variance. The modelling case study of a gas–liquid separator is presented in this paper. It describes the comparison of three methods for dynamic GP model simulation in the phase of model validation. The level of the computational burden associated with each approach increases with the complexity of the computation necessary for an approximation of the uncertainty propagation.

Related Topics
Physical Sciences and Engineering Computer Science Computer Science (General)
Authors
, ,