Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes

2012 ◽  
Vol 106 (1) ◽  
pp. 55-66 ◽  
Author(s):  
J. Fernandez de Canete ◽  
S. Gonzalez-Perez ◽  
J.C. Ramos-Diaz
Keyword(s):  
Neural Networks ◽  
Type 1 Diabetes ◽  
Artificial Neural Networks ◽  
In Silico ◽  
Closed Loop ◽  
Ad Hoc ◽  
Closed Loop Control ◽  
Loop Control ◽  
Artificial Neural

In Silico Closed-Loop Control Validation Studies for Optimal Insulin Delivery in Type 1 Diabetes

2015 ◽  
Vol 62 (10) ◽  
pp. 2369-2378 ◽  
Author(s):  
Stamatina Zavitsanou ◽  
Athanasios Mantalaris ◽  
Michael C. Georgiadis ◽  
Efstratios N. Pistikopoulos
Keyword(s):  
Type 1 Diabetes ◽  
Validation Studies ◽  
In Silico ◽  
Closed Loop ◽  
Insulin Delivery ◽  
Closed Loop Control ◽  
Loop Control

scholarly journals Exercise-induced hypoglycemia in type 1 diabetes: in-silico comparison between announced and unannounced strategies in closed-loop control

2019 ◽  
Vol 52 (1) ◽  
pp. 1000-1005 ◽  
Author(s):  
Arthur Bertachi ◽  
Charrise M. Ramkissoon ◽  
Aleix Beneyto ◽  
Josep Vehí
Keyword(s):  
Type 1 Diabetes ◽  
In Silico ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Loop Control ◽  
Exercise Induced

Evolutionary Optimization of Artificial Neural Networks for Prosthetic Knee Control

2013 ◽  
pp. 142-161 ◽  
Author(s):  
George Thomas ◽  
Timothy Wilmot ◽  
Steve Szatmary ◽  
Dan Simon ◽  
William Smith
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Closed Loop ◽  
Knee Prosthesis ◽  
Open Loop ◽  
Closed Loop Control ◽  
Open Loop Control ◽  
Loop Control ◽  
Prosthetic Knee ◽  
Artificial Neural

This chapter discusses closed-loop control development and simulation results for a semi-active above-knee prosthesis. This closed-loop control is a delta control that is added to previously developed open-loop control. The control signal consists of two hydraulic valve settings. These valves control a rotary actuator that provides torque to the prosthetic knee. Closed-loop control using artificial neural networks (ANNs) are developed, which is an intelligent control method. The ANNs are trained with biogeography-based optimization (BBO), which is a recently developed evolutionary algorithm. This research contributes to the field of evolutionary algorithms by demonstrating that BBO is successful at finding optimal solutions to real-world, nonlinear, time varying control problems. The research contributes to the field of prosthetics by showing that it is possible to find effective closed-loop control signals for a newly proposed semi-active hydraulic knee prosthesis. The research also contributes to the field of ANNs; it shows that they are able to mitigate some of the effects of noise and disturbances that will be common in normal operation of a prosthesis and that they can provide better robustness and safer operation with less risk of stumbles and falls. It is demonstrated that ANNs are able to improve average performance over open-loop control by up to 8% and that they show the greatest improvement in performance when there is high risk of stumbles.

scholarly journals In Silico Preclinical Trials: Methodology and Engineering Guide to Closed-Loop Control in Type 1 Diabetes Mellitus

2009 ◽  
Vol 3 (2) ◽  
pp. 269-282 ◽  
Author(s):  
Stephen D. Patek ◽  
B. Wayne Bequette ◽  
Marc Breton ◽  
Bruce A. Buckingham ◽  
Eyal Dassau ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
In Silico ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Loop Control ◽  
Preclinical Trials ◽  
Trials Methodology

scholarly journals Closed-loop control with unannounced exercise for adults with type 1 diabetes using the Ensemble Model Predictive Control

2019 ◽  
Vol 80 ◽  
pp. 202-210 ◽  
Author(s):  
Jose Garcia-Tirado ◽  
John P. Corbett ◽  
Dimitri Boiroux ◽  
John Bagterp Jørgensen ◽  
Marc D. Breton
Keyword(s):  
Type 1 Diabetes ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Ensemble Model ◽  
Loop Control
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