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Diniz, R. C.; Serra, G. L. O.;

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Diabetes is one of the most serious public health problems of today. According to the IDF (International Diabetes Federation) in a study published recently, Brazil currently occupies the 5th place in the world list of nations with the largest number of sufferers[1]. A fuzzy controller in the context of diabetes plays the role of deciding the amount of insulin needed to keep the body functioning, since the pancreas is not performing its function properly. The main advantage of using fuzzy control systems is based on the fact that the mathematical equations are not able to correctly simulate a biological system, because the parameters of system are enough variables because they are open thermodynamic systems, which have factors internals and externals interference[2]. For better analysis of the control system based on fuzzy logic, we used the clinical data available from the University of California at San Diego in the Project Monitoring and Control Glucose in Humans. In the control plant was used as a dynamic model of the patient actual data and not a mathematical model that represents a biological system[3]. In this manner can be analyzed the efficiency of a fuzzy controller made from an artificial model (a set of mathematical equations) for the glucose-insulin system and the sensitivity level of control compared to Chein and Tsai 2010[4]. The results satisfactorily met the goal in question, in other words, to keep the rate glucose in physiologically normal level using data from clinical patients of Project Monitoring Glycemic Control and the University of San Diego through a fuzzy controller made from a model mathematical for glucose-insulin system.

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Palavras-chave: Glucose-Insulin System, Diabetes, Fuzzy Control.,


DOI: 10.5151/meceng-wccm2012-19464

Referências bibliográficas
  • [1] Brown, L., Edelman, E. R. Optimal Control of Blood Glucose: The Diabetic Patient or the Machine?, Science Translational Medicine, 27 (2010), 559 – 573.
  • [2] MAKROGLOU,Athena; LI, Jiaxu; KUANG, Yang. Mathematical models and software tools for the glucose-insulin regulatory system and diabetes: na overview. Applied Numerical Mathematics 56, 2006. 559-573 p.
  • [3] Searched: http://glucosecontrol.ucsd.edu/data.html. Acesso em 01.11.11.
  • [4] Chen, C. L., Tsai, H. W. Modeling the physiological glucose-insulin system on normal and diabetic subjects, Computer Methods and Programs in Biomedicine, 97 (2010), 130 – 140.
  • [5] Wang. A Course in Fuzzy Systems and Control. Prentice Hall, 1996.
Como citar:

Diniz, R. C.; Serra, G. L. O.; "DIABETIC CONTROL VIA FUZZY SYSTEMS", p. 3549-3553 . In: In Proceedings of the 10th World Congress on Computational Mechanics [= Blucher Mechanical Engineering Proceedings, v. 1, n. 1]. São Paulo: Blucher, 2014.
ISSN 2358-0828, DOI 10.5151/meceng-wccm2012-19464

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