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ABC Algorithm based Fuzzy Modeling of Optical Glucose DetectionSARACOGLU, O. G. , BAGIS, A. , KONAR, M. , TABARU, T. E.
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fuzzy systems, heuristic algorithms, evolutionary computation, optical sensors, computational modeling
fuzzy(21), glucose(13), systems(12), biosensors(9), algorithm(9), measurement(8), modeling(7), control(7), vivo(6), system(5)
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About this article
Date of Publication: 2016-08-31
Volume 16, Issue 3, Year 2016, On page(s): 37 - 42
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.03006
Web of Science Accession Number: 000384750000006
SCOPUS ID: 84991111440
This paper presents a modeling approach based on the use of fuzzy reasoning mechanism to define a measured data set obtained from an optical sensing circuit. For this purpose, we implemented a simple but effective an in vitro optical sensor to measure glucose content of an aqueous solution. Measured data contain analog voltages representing the absorbance values of three wavelengths measured from an RGB LED in different glucose concentrations. To achieve a desired model performance, the parameters of the fuzzy models are optimized by using the artificial bee colony (ABC) algorithm. The modeling results presented in this paper indicate that the fuzzy model optimized by the algorithm provide a successful modeling performance having the minimum mean squared error (MSE) of 0.0013 which are in clearly good agreement with the measurements.
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 Improved artificial bee colony algorithm with opposition‐based learning, Cao, Yongcun, Ji, Saisai, Lu, Yong, IET Image Processing, ISSN 1751-9659, Issue 15, Volume 14, 2020.
Digital Object Identifier: 10.1049/iet-ipr.2020.0111 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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