Introduction to Artificial Neural Systems
Introduction to Artificial Neural Systems
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Mastering SIMULINK
Model Reference Adaptive Control on Glucose for the Treatment of Diabetes Mellitus
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Applied Numerical Mathematics
Feedforward-feedback multiple predictive controllers for glucose regulation in type 1 diabetes
Computer Methods and Programs in Biomedicine
A new general glucose homeostatic model using a proportional-integral-derivative controller
Computer Methods and Programs in Biomedicine
Physiologic insulin delivery with insulin feedback: A control systems perspective
Computer Methods and Programs in Biomedicine
Neural-network models for the blood glucose metabolism of a diabetic
IEEE Transactions on Neural Networks
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The closed loop control of blood glucose levels might help to reduce many short- and long-term complications of type 1 diabetes. Continuous glucose monitoring and insulin pump systems have facilitated the development of the artificial pancreas. In this paper, artificial neural networks are used for both the identification of patient dynamics and the glycaemic regulation. A subcutaneous glucose measuring system together with a Lispro insulin subcutaneous pump were used to gather clinical data for each patient undergoing treatment, and a corresponding in silico and ad hoc neural network model was derived for each patient to represent their particular glucose-insulin relationship. Based on this nonlinear neural network model, an ad hoc neural network controller was designed to close the feedback loop for glycaemic regulation of the in silico patient. Both the neural network model and the controller were tested for each patient under simulation, and the results obtained show a good performance during food intake and variable exercise conditions.