Skip to main content
padlock icon - secure page this page is secure

Dynamic systems identification with Gaussian processes

Buy Article:

$61.00 + tax (Refund Policy)

This paper describes the identification of nonlinear dynamic systems with a Gaussian process (GP) prior model. This model is an example of the use of a probabilistic non-parametric modelling approach. GPs are flexible models capable of modelling complex nonlinear systems. Also, an attractive feature of this model is that the variance associated with the model response is readily obtained, and it can be used to highlight areas of the input space where prediction quality is poor, owing to the lack of data or complexity (high variance). We illustrate the GP modelling technique on a simulated example of a nonlinear system.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: Gaussian processes; Nonlinear dynamic systems; System identification

Document Type: Research Article

Affiliations: 1: Hamilton Institute, Natural University of Ireland, Maynooth, Ireland 2: Department of Computer Science, University of Glasgow, Glasgow, UK 3: Jozef Stefan Institute, Ljubljana, Slovenia

Publication date: December 1, 2005

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more