Derivative observations in Gaussian Process models of dynamic systems
E Solak, R. Murray-Smith, W. Leithead and D. Leith
In: NIPS 15,, November 2002, Vancouver, Canada.
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 process.
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 consistent
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.