Dynamic systems identification with Gaussian processes

Kocijan, J., Girard, A., Banko, B. and Murray-Smith, R. (2005) Dynamic systems identification with Gaussian processes. Mathematical and Computer Modelling of Dynamical Systems, 11(4), pp. 411-424. (doi:10.1080/13873950500068567)

Full text not currently available from Enlighten.

Abstract

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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick
Authors: Kocijan, J., Girard, A., Banko, B., and Murray-Smith, R.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Mathematical and Computer Modelling of Dynamical Systems
ISSN:1387-3954
ISSN (Online):1744-5051

University Staff: Request a correction | Enlighten Editors: Update this record