The application of an artificial neural network in the identification of medicinal rhubarbs by near-infrared spectroscopy.
The application of an artificial neural network in the identification of medicinal rhubarbs by near-infrared spectroscopy.
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DOI：
10.1002/pca.654
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发表时间：
2002-09
期刊：
影响因子：
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通讯作者：
L. Xiang;Guo-qiang Fan;Junhui Li;Hui Kang;Yanlu Yan;Jun-hua Zheng;D. Guo
中科院分区：
文献类型：
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作者：
L. Xiang;Guo-qiang Fan;Junhui Li;Hui Kang;Yanlu Yan;Jun-hua Zheng;D. Guo
本文介绍了一种将近红外光谱与三层反向传播人工神经网络相结合的方法来鉴别官方大黄和非官方大黄。取33个样本作为训练集，62个样本作为测试集。确定了输入节点数、学习率和动量对训练集的最终误差和识别精度以及对测试集的预测精度的影响。一个具有8个输入节点、0.5学习率、0.3动量的神经网络，对训练集的识别准确率为100%，对测试集的预测准确率为96.8%。该方法为大黄的鉴别提供了一种快速有效的方法。
This paper describes a method to combine near-infrared spectroscopy and a three layer back-propagation artificial neural network in order to identify official and unofficial rhubarbs. Thirty-three samples were taken as the training set, and 62 samples as the test set. The effects of input node number, learning rate and momentum on the final error and recognition accuracy for the training set, and on prediction accuracy for the test set were determined. A neural network with eight input nodes, a 0.5 learning rate, and a momentum of 0.3 can achieve a recognition accuracy of 100% for the training set and a prediction accuracy of 96.8% for the test set. The method described offers a quick and efficient means of identifying rhubarbs.