eprintid: 34199 rev_number: 16 eprint_status: archive userid: 558 dir: disk0/00/03/41/99 datestamp: 2010-08-04 10:08:42 lastmod: 2021-09-22 10:07:40 status_changed: 2010-08-04 10:08:42 type: article metadata_visibility: show item_issues_count: 0 creators_name: Kocijan, J. creators_name: Girard, A. creators_name: Banko, B. creators_name: Murray-Smith, R. creators_orcid: 0000-0003-4228-7962 title: Dynamic systems identification with Gaussian processes ispublished: pub subjects: QA75 divisions: 30200000 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. date: 2005-12 date_type: published id_number: 10.1080/13873950500068567 uniqueid: glaseprints:2005-34199 issn_online: 1744-5051 scopus_impact: 35 scopus_cluster: 2-s2.0-29244441730 scopus_datestamp: 2013-10-25 23:28:03 wos_impact: 21 wos_cluster: WOS:000233228600003 wos_datestamp: 2013-10-25 00:16:11 full_text_status: none publication: Mathematical and Computer Modelling of Dynamical Systems volume: 11 number: 4 pagerange: 411-424 event_type: other refereed: TRUE issn: 1387-3954 hoa_compliant: 305 hoa_date_pub: 2005-12 hoa_exclude: FALSE hoa_gold: FALSE citation: 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 )