Derivative observations in Gaussian Process models of dynamic systems

Solak, E., Murray-Smith, R., Leithead, W.E., Leith, D.J. and Rasmussen, C.E. (2003) Derivative observations in Gaussian Process models of dynamic systems. In: Conference on Neural Information Processing Systems, Vancouver, Canada, 9-14 December 2002, ISBN 0262112450




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 efficiency of Gaussian process models for dynamic system identification, by summarising large quantities of near-equilibrium data by a handful of linearisations, reducing the training size - traditionally a problem for Gaussian process models.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick
Authors: Solak, E., Murray-Smith, R., Leithead, W.E., Leith, D.J., and Rasmussen, C.E.
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
College/School:College of Science and Engineering > School of Computing Science
Publisher:MIT Press
Copyright Holders:Copyright © 2002 MIT Press
First Published:First published in London
Publisher Policy:Reproduced with the permission of the publisher

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