Nonlinear identification of glucose absorption related to Diabetes Mellitus

In case of biomedical researches we often have to deal with complicated biological phenomenons, which are usually described with complex mathematical models. In most cases these mathematical models and the systems to be modelled are also nonlinear. The appropriate adjustment of the parameters of these models is always a problem which is hard to be solved. To work with such complex models is essential in many research fields and application areas e.g. in personalized medicine or by the control of physiological processes. Although there are many identification techniques available, there is no general or “oven-ready” solution in cases where the mathematical model describing the dynamics of the physiological processes is highly nonlinear. One of our aims was to develop a simple, user-friendly and flexible identification framework which supports the identification of complex, nonlinear mathematical models. The performance of the method can be measured by simple metric. On the other hand, our goal was to successfully realize the identification framework in case of glucose absorption models, which are essential in our future work in order to validate the performance of advanced control algorithms. Our results show that the nonlinear identification framework performed well, since the predefined requirements were satisfied in all cases.