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On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico TestingRIOS, Y. , GARCIA-RODRIGUEZ, J. , SANCHEZ, E. , ALANIS, A. , RUIZ-VELAZQUEZ, E. , PARDO-GARCIA, A.
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biomedical electronics, biomedical monitoring, fuzzy neural networks, neurocontrollers, virtual prototyping
control(39), diabetes(29), systems(16), optimal(15), sanchez(13), neural(13), inverse(12), type(10), fuzzy(9), networks(7)
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About this article
Date of Publication: 2022-08-31
Volume 22, Issue 3, Year 2022, On page(s): 3 - 14
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.03001
Web of Science Accession Number: 000861021000001
SCOPUS ID: 85137729315
Type 1 Diabetes Mellitus (T1DM) is one of the most adverse diseases in the modern era; its treatment is mainly based on exogenous insulin injections. The scientific community has formulated strategies to improve insulin supply using state-of-the-art technology. Therefore, this article develops a multi-age glycemic control scheme, which can be implemented in an Artificial Pancreas (AP) device to enhance diabetics treatment. The procedure is based on the implementation of a neuro-fuzzy inverse optimal control (NF-IOC) algorithm on the Texas Instrument LAUNCHXL-F28069M development board; this controller communicates with the Uva/Padova simulator for diabetics' patients of different ages under predefined meal protocols running on a Personal Computer (PC). The novelty lies in the proposed NF-IOC capability to regulate glucose within safe levels for virtual populations of 10 adults, 10 adolescents and, 10 children.
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