基于梯度下降反向传播算法的人工神经网络蜂蜜真假分类

Carlos C. Hortinela, Jessie R. Balbin, P. A. Tibayan, John Myrrh D. Cabela, G. Magwili
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引用次数: 1

摘要

蜂蜜一直是世界各地食品欺诈的名单之一。一名举报人出现在南非蜂蜜制造商,声称他们的蜂蜜被发现是伪造的糖混合物。此外,2016年中期,菲律宾有一家名为Cem's honey的蜂蜜制造商将他们的产品误标为真蜂蜜,但实际上他们的产品是假蜂蜜。本研究的主要目的是利用梯度下降反向传播的人工神经网络作为训练算法,以及传感器(电导率和pH传感器),创建一个可以对蜂蜜进行真假分类的系统。通过测试,该系统对呈现的样本进行分类,准确率达到87.5%。总之,研究人员成功开发了一个系统,可以使用人工神经网络对蜂蜜进行真假分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Honey as Genuine or Fake via Artificial Neural Network using Gradient Descent Backpropagation Algorithm
Honey is always among the lists for food fraud around the world. A whistleblower surfaced in a South African Honey manufacturer that claims their honey is revealed as passing off a sugar concoction. Also, in the Philippines, mid-2016 there's a manufacturer of honey named Cem's Honey that mislabels their product as real honey but in fact their product is a fake honey. The main objective of this study is to create a system that could classify a honey whether it is genuine or fake using Artificial Neural Network with Gradient Descent Backpropagation as the training algorithm, and sensors (Electrical Conductivity and pH Sensor). Through testing, the system classified the presented samples at an accuracy rate of 87.5%. In conclusion, the researchers successfully developed a system that can classify a honey whether is it genuine or fake using Artificial Neural Network.
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