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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2016 - 6

ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection

SARACOGLU, O. G. See more information about SARACOGLU, O. G. on SCOPUS See more information about SARACOGLU, O. G. on IEEExplore See more information about SARACOGLU, O. G. on Web of Science, BAGIS, A. See more information about  BAGIS, A. on SCOPUS See more information about  BAGIS, A. on SCOPUS See more information about BAGIS, A. on Web of Science, KONAR, M. See more information about  KONAR, M. on SCOPUS See more information about  KONAR, M. on SCOPUS See more information about KONAR, M. on Web of Science, TABARU, T. E. See more information about TABARU, T. E. on SCOPUS See more information about TABARU, T. E. on SCOPUS See more information about TABARU, T. E. on Web of Science
 
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Download PDF pdficon (1,221 KB) | Citation | Downloads: 990 | Views: 3,959

Author keywords
fuzzy systems, heuristic algorithms, evolutionary computation, optical sensors, computational modeling

References keywords
fuzzy(21), glucose(13), systems(12), biosensors(9), algorithm(9), measurement(8), modeling(7), control(7), vivo(6), system(5)
Blue keywords are present in both the references section and the paper title.

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

Abstract
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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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Cited-By CrossRef

[1] Improved artificial bee colony algorithm with oppositionā€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
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Faculty of Electrical Engineering and Computer Science
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