Divide and conquer identification using Gaussian process priors

D.J. Leith, W.E. Leithead, D. Murray-smith

Research output: Contribution to conferencePaper

14 Downloads (Pure)

Abstract

We investigate the reconstruction of nonlinear systems from locally identified linear models. It is well known that the equilibrium linearisations of a system do not uniquely specify the global dynamics. Information about the dynamics near to equilibrium provided by the equilibrium linearisations is therefore combined with other information about the dynamics away from equilibrium provided by suitable measured data. That is, a hybrid local/global modelling approach is considered. A non-parametric Gaussian process prior approach is proposed for combining in a consistent manner these two distinct types of data. This approach seems to provide a framework that is both elegant and powerful, and which is potentially in good accord with engineering practice.
Original languageEnglish
Pages623-629
Number of pages7
DOIs
Publication statusPublished - 2002
Event 41st IEEE Conference on Decision and Control - Las Vegas, United States
Duration: 10 Dec 200213 Dec 2002

Conference

Conference 41st IEEE Conference on Decision and Control
Country/TerritoryUnited States
CityLas Vegas
Period10/12/0213/12/02

Keywords

  • Gaussian process priors
  • nonlinear systems
  • divide and conquer methods

Fingerprint

Dive into the research topics of 'Divide and conquer identification using Gaussian process priors'. Together they form a unique fingerprint.

Cite this