Derivative observations in Gaussian process models of dynamic systems

Solak, E. and Murray-Smith, R. and Leithead, W.E. and Rasmusson, C. and Leith, D.J. (2002) Derivative observations in Gaussian process models of dynamic systems. In: 2002 Neural Information Processing (NIPS) Meeting, 2002-12-09 - 2002-12-11.

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.

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• Item metadata

  Item type:	Conference or Workshop Item(Paper)
  ID code:	38601
  Dates:	Date Event 2002 Published
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering
  Department:	Faculty of Engineering > Electronic and Electrical Engineering
  Depositing user:	Pure Administrator
  Date deposited:	20 Mar 2012 15:08
  Last modified:	11 Nov 2024 16:16
  Related URLs:	Event
  URI:	https://strathprints.strath.ac.uk/id/eprint/38601