On Data-driven Multistep Subspace-based Linear Predictors

Summary, in English

The focus of this contribution is the estimation of multi-step-ahead linear multivariate predictors of the output making use of finite input-output data sequences. Different strategies will be presented, the common factor being the exploitations of geometric operations on appropriate subspaces spanned by the data. In order to test the capabilities of the proposed methods in predicting new data, a real-life example, namely, the case of blood glucose prediction in Type 1 Diabetes patients, is provided.

• Författare: Marzia Cescon; Rolf Johansson
• Avdelning/ar: Institutionen för reglerteknik; ELLIIT: the Linköping-Lund initiative on IT and mobile communication
• Publiceringsår: 2011
• Språk: Engelska
• Sidor: 11447-11452
• Publikation/Tidskrift/Serie: IFAC Proceedings Volumes
• Volym: 44
• Issue: 1
• Fulltext: - 405 kB; Download statistics
• Länkar: http://dx.doi.org/10.3182/20110828-6-IT-1002.02828
• Dokumenttyp: Konferensbidrag
• Förlag: Elsevier
• Ämne: Control Engineering
• Nyckelord: Subspace identification; prediction error methods; biological systems
• Conference name: 18th IFAC World Congress, 2011
• Conference date: 2011-08-28 - 2011-09-02
• Conference place: Milan, Italy
• Status: Published
• Projekt: DIAdvisor; DIAdvisor
• Forskningsgrupp: LCCC
• ISBN/ISSN/Övrigt: ISSN: 1474-6670; ISBN: 978-3-902661-93-7