Robust model identification applied to type 1diabetes

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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.
Original languageEnglish
Title of host publicationProceedings of the American Control Conference
PublisherIEEE
Publication date2010
Pages2021-2021
ISBN (Print)978-1-4244-7426-4
Publication statusPublished - 2010
EventAmerican Control Conference (ACC 2010) - Baltimore, MD, United States
Duration: 3 Jun 20102 Jul 2010
http://a2c2.org/conferences/acc2010/

Conference

ConferenceAmerican Control Conference (ACC 2010)
CountryUnited States
CityBaltimore, MD
Period03/06/201002/07/2010
Internet address

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