Name
Title | ||
|---|---|---|
Glycaemia Regulation Predictive Control Systems Performances Evaluation A Comparative Study Of Neural And Mathematical Models |
Abstract | ||
|---|---|---|
Type I blood glucose regulation remains a complex problem to simulate. Different blood glucose control schemes for insulin-dependent diabetes therapies and systems have been proposed in the literature. This article presents an adaptative predictive control system for glycaemia regulation based on feedforward Artificial Neural Networks trained with the resilient propagation (RPROP) method. Experiments performed on a mathematical (theoretical) compensation model and our system aim to objectively compare the behaviour of each approach when both exact and perturbated data are presented. These experiments, which make use of a virtual patient, not only cover the ANN's best configuration and training parameters on exact training information, they also demonstrate the accuracy of the neural approach when up to 20% perturbated data are supplied. As a result of the experiments on perturbated data, the neural approach gives slightly better evaluations than the theoretical model. This demonstrates the neural system's ability to adapt to perturbated environments. |
Year | Venue | Keywords |
|---|---|---|
2011 | HEALTHINF 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON HEALTH INFORMATICS | Diabetes, Glycaemia regulation, Insulin, NPC, Artificial neural network, Resilient propagation |
Field | DocType | Citations |
Data mining,Simulation,Computer science,Model predictive control,Artificial intelligence,Mathematical model,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nathanael Cottin | 1 | 5 | 2.87 |
| O. Grunder | 2 | 23 | 6.19 |
| Abdellah El Moudni | 3 | 153 | 26.13 |