NDLI: Robust model identification applied to type 1 diabetes

Content Provider	IEEE Xplore Digital Library
Author	Finan, D.A. Jørgensen, J.B. Poulsen, N.K. Madsen, H.
Copyright Year	2010
Description	Author affiliation: Dept. of Inf. & Math. Modelling, Tech. Univ. of Denmark, Lyngby, Denmark (Finan, D.A.; Jørgensen, J.B.; Poulsen, N.K.; Madsen, H.)
Abstract	In many realistic applications, process noise is known to be neither white nor normally distributed. When identifying models in these cases, it may be more effective to minimize a different penalty function than the standard sum of squared errors (as in a least-squares identification method). This paper investigates model identification based on two different penalty functions: the 1-norm of the prediction errors and a Huber-type penalty function. For data characteristic of some realistic applications, model identification based on these latter two penalty functions is shown to result in more accurate estimates of parameters than the standard least-squares solution, and more accurate model predictions for test data. The identification techniques are demonstrated on a simple toy problem as well as a physiological model of type 1 diabetes.
Starting Page	2021
Ending Page	2026
File Size	392668
Page Count	6
File Format	PDF
ISBN	9781424474264
ISSN	07431619
e-ISBN	9781424474271
DOI	10.1109/ACC.2010.5531635
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Robustness Diabetes Predictive models Sugar Biomedical measurements Mathematical model Sensor phenomena and characterization Parameter estimation Insulin Testing
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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