Derivative observations in Gaussian process models of dynamic systems

E. Solak, R. Murray-Smith, W.E. Leithead, C. Rasmusson, D.J. Leith

Research output: Contribution to conferencePaper

Abstract

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.

LanguageEnglish
Number of pages8
Publication statusPublished - 2002
Event2002 Neural Information Processing (NIPS) Meeting - Vancouver, Canada
Duration: 9 Dec 200211 Dec 2002

Conference

Conference2002 Neural Information Processing (NIPS) Meeting
CountryCanada
CityVancouver
Period9/12/0211/12/02

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learning
modeling

Keywords

  • derivative observations
  • gaussian process models
  • dynamic systems

Cite this

Solak, E., Murray-Smith, R., Leithead, W. E., Rasmusson, C., & Leith, D. J. (2002). Derivative observations in Gaussian process models of dynamic systems. Paper presented at 2002 Neural Information Processing (NIPS) Meeting , Vancouver, Canada.
Solak, E. ; Murray-Smith, R. ; Leithead, W.E. ; Rasmusson, C. ; Leith, D.J. / Derivative observations in Gaussian process models of dynamic systems. Paper presented at 2002 Neural Information Processing (NIPS) Meeting , Vancouver, Canada.8 p.
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Solak, E, Murray-Smith, R, Leithead, WE, Rasmusson, C & Leith, DJ 2002, 'Derivative observations in Gaussian process models of dynamic systems' Paper presented at 2002 Neural Information Processing (NIPS) Meeting , Vancouver, Canada, 9/12/02 - 11/12/02, .

Derivative observations in Gaussian process models of dynamic systems. / Solak, E.; Murray-Smith, R.; Leithead, W.E.; Rasmusson, C.; Leith, D.J.

2002. Paper presented at 2002 Neural Information Processing (NIPS) Meeting , Vancouver, Canada.

Research output: Contribution to conferencePaper

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AU - Murray-Smith, R.

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AU - Rasmusson, C.

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AB - 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.

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Solak E, Murray-Smith R, Leithead WE, Rasmusson C, Leith DJ. Derivative observations in Gaussian process models of dynamic systems. 2002. Paper presented at 2002 Neural Information Processing (NIPS) Meeting , Vancouver, Canada.