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)

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