Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
E. Solak, R. Murray-smith, W. Leithead, D. Leith, Carl Rasmussen
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identiﬁcation of nonlinear dynamic systems from experimental data. 1) It allows us to combine derivative information, and associated uncertainty with normal function observations into the learning and inference pro- cess. This derivative information can be in the form of priors speciﬁed by an expert or identiﬁed from perturbation data close to equilibrium. 2) It allows a seamless fusion of multiple local linear models in a consis- tent manner, inferring consistent models and ensuring that integrability constraints are met. 3) It improves dramatically the computational ef- ﬁciency of Gaussian process models for dynamic system identiﬁcation, by summarising large quantities of near-equilibrium data by a handful of linearisations, reducing the training set size – traditionally a problem for Gaussian process models.