PROJECT TITLE :

A Nonlinear Blind Identification Approach to Modeling of Diabetic Patients

ABSTRACT:

Within the last decades, mathematical models became of great importance within the context of diabetes treatment designing. Many modeling approaches primarily based on initial principles or input–output techniques have been proposed. But, a relevant open downside common to all or any these approaches is that they're not in a position to recover or to systematically account for the varied unmeasured signals that have an effect on a diabetic patient (e.g., food, physical activity, and emotions). A novel blind identification approach is introduced during this transient, allowing us to model type 1 diabetic patients and to effectively recover the unmeasured input signals. The approach is applied to an experimental study concerning identification and prediction of the blood glucose concentration in five sort one diabetic patients.