Glucose-Insulin regulator for Type 1 Diabetes using high order neural networks

AGUSTÍN, RODRÍ´IGUEZ-HERRERO and CARLOS, E. CASTAÑEDA and GEMA, GARCÍA-SAÉZ and M. ELENA, HERNANDO and ONOFRE, OROZCO (2014) Glucose-Insulin regulator for Type 1 Diabetes using high order neural networks. In: International Conference on Advances in Computing, Communication and Information Technology CCIT 2014, 01 - 02 June,2014, London, UK.

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Abstract

In this paper a Glucose-Insulin regulator for Type 1 Diabetes using artificial neural networks (ANN) is proposed. This is done using a discrete recurrent high order neural network in order to identify and control a nonlinear dynamical system which represents the pancreas’ beta-cells behavior of a virtual patient. The ANN which reproduces and identifies the dynamical behavior system, is configured as series parallel and trained on line using the extended Kalman filter algorithm to achieve a quickly convergence identification in silico. The control objective is to regulate the glucose-insulin level under different glucose inputs and is based on a nonlinear neural block control law. A safety block is included between the control output signal and the virtual patient with type 1 diabetes mellitus. Simulations include a period of three days. Simulation results are compared during the overnight fasting period in Open-Loop (OL) versus Closed-Loop (CL). Tests in Semi-Closed-Loop (SCL) are made feed-forward in order to give information to the control algorithm. We conclude the controller is able to drive the glucose to target in overnight periods and the feedforward is necessary to control the postprandial period.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Identification, Recurrent Neural Networks, Extended Kalman, Diabetes, Artificial Pancreas, insulin, glucose
Depositing User: Mr. John Steve
Date Deposited: 18 May 2019 12:20
Last Modified: 18 May 2019 12:20
URI: http://publications.theired.org/id/eprint/2465

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