Design of a BR-ABC Algorithm-Based Fuzzy Model for Glucose Detection

This paper presents a modeling approach for defining a measured data set obtained from an optical sensing circuit based on the use of a fuzzy reasoning system. A simple but effective optical sensor is designed for in vitro determination of glucose concentrations in an aqueous solution. The measured...

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Bibliographic Details
Published in:Augmented human research 2020-12, Vol.5 (1), Article 17
Main Authors: Gupta, Bhumika, Verma, Agya Ram
Format: Article
Language:English
Subjects:
Biomedical Engineering and Bioengineering
Cognitive Psychology
Computational Intelligence
Emerging trends in Computational Intelligence and Complexity
Engineering
Human Physiology
Original Paper
Robotics and Automation
User Interfaces and Human Computer Interaction
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Summary:This paper presents a modeling approach for defining a measured data set obtained from an optical sensing circuit based on the use of a fuzzy reasoning system. A simple but effective optical sensor is designed for in vitro determination of glucose concentrations in an aqueous solution. The measured data used in this study include analog voltages that reflect the absorbance values of three wavelengths measured in different concentrations of glucose from an RGB light-emitting diode (LED). The parameters of the fuzzy models are optimized using the bounded-range artificial bee colony (BR-ABC) algorithm to achieve the desired model performance. The results indicate that the optimized fuzzy model demonstrates high performance quality. The minimum mean square error (MSE) obtained from the singleton fuzzy model with the BR-ABC algorithm is 0.00014, which is better than the reported MSE value achieved with the Takagi–Sugeno fuzzy model.
ISSN:2365-4317
2365-4325
DOI:10.1007/s41133-019-0026-1