ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection
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. Measur...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Stefan cel Mare University of Suceava
2016-08-01
|
Series: | Advances in Electrical and Computer Engineering |
Subjects: | |
Online Access: | http://dx.doi.org/10.4316/AECE.2016.03006 |
Summary: | 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. |
---|---|
ISSN: | 1582-7445 1844-7600 |