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On Data-driven Multistep Subspace-based Linear Predictors

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Author

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.

Publishing year

2011

Language

English

Pages

11447-11452

Publication/Series

IFAC Proceedings Volumes

Volume

44

Issue

1

Document type

Conference paper

Publisher

Elsevier

Topic

  • Control Engineering

Keywords

  • 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

Project

  • DIAdvisor
  • DIAdvisor

Research group

  • LCCC

ISBN/ISSN/Other

  • ISSN: 1474-6670
  • ISBN: 978-3-902661-93-7