On Data-driven Multistep Subspace-based Linear Predictors

Marzia Cescon, Rolf Johansson

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Abstract

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.
Original languageEnglish
Title of host publicationIFAC Proceedings Volumes
PublisherElsevier
Pages11447–11452
Volume44
Edition1
DOIs
Publication statusPublished - 2011
Event18th IFAC World Congress, 2011 - Milan, Italy
Duration: 2011 Aug 282011 Sep 2
Conference number: 18

Conference

Conference18th IFAC World Congress, 2011
Country/TerritoryItaly
CityMilan
Period2011/08/282011/09/02

Subject classification (UKÄ)

  • Control Engineering

Keywords

  • Subspace-identification
  • prediction error methods
  • biological systems

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