Robust model identification applied to type 1diabetes

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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
Publication date2010
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


ConferenceAmerican Control Conference (ACC 2010)
Country/TerritoryUnited States
CityBaltimore, MD
Internet address


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