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On Data-driven Multistep Subspace-based Linear Predictors

Cescon, Marzia LU and Johansson, Rolf LU (2011) 18th IFAC World Congress, 2011
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.
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author
organization
publishing date
type
Contribution to conference
publication status
published
subject
keywords
Subspace-identification, prediction error methods, biological systems
conference name
18th IFAC World Congress, 2011
external identifiers
  • Scopus:84866747262
project
DIAdvisor
language
English
LU publication?
yes
id
1ca0f1ee-67b2-4f9a-86af-d2324f1cc140 (old id 2203125)
date added to LUP
2011-11-08 09:26:48
date last changed
2016-10-13 05:01:50
@misc{1ca0f1ee-67b2-4f9a-86af-d2324f1cc140,
  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.},
  author       = {Cescon, Marzia and Johansson, Rolf},
  keyword      = {Subspace-identification,prediction error methods,biological systems},
  language     = {eng},
  title        = {On Data-driven Multistep Subspace-based Linear Predictors},
  year         = {2011},
}