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Derivative observations in Gaussian Process models of dynamic systems

2003

Conference Paper

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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 set size - traditionally a problem for Gaussian process models.

Author(s): Solak, E. and Murray-Smith, R. and Leithead, WE. and Leith, D. and Rasmussen, CE.
Book Title: Advances in Neural Information Processing Systems 15
Journal: Advances in Neural Information Processing Systems 15
Pages: 1033-1040
Year: 2003
Month: October
Day: 0
Editors: Becker, S., S. Thrun and K. Obermayer
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-02550-7
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2107,
  title = {Derivative observations in Gaussian Process models of dynamic systems},
  author = {Solak, E. and Murray-Smith, R. and Leithead, WE. and Leith, D. and Rasmussen, CE.},
  journal = {Advances in Neural Information Processing Systems 15},
  booktitle = {Advances in Neural Information Processing Systems 15},
  pages = {1033-1040},
  editors = {Becker, S., S. Thrun and K. Obermayer},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = oct,
  year = {2003},
  month_numeric = {10}
}