A comparative study of multilayer perceptron neural networks for the identification of rhubarb samples.
A comparative study of multilayer perceptron neural networks for the identification of rhubarb samples.
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DOI：
10.1002/pca.957
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发表时间：
2007-03
期刊：
影响因子：
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通讯作者：
Zhuoyong Zhang;Yamin Wang;Guo-qiang Fan;P. Harrington
中科院分区：
文献类型：
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作者：
Zhuoyong Zhang;Yamin Wang;Guo-qiang Fan;P. Harrington
人工神经网络作为一种快速、灵活的传统医药质量控制方法，近年来受到了广泛的关注。近红外光谱分析技术具有简便、快速、无损等优点，已成为目前公认的中药定性和定量分析方法。介绍了一种将三层感知器神经网络应用于近红外光谱数据的大黄正品与非正品的鉴别方法。采用BP、Delta-bar-Delta和快速传播算法对多层感知器神经网络进行训练。使用这些方法得到的结果都是令人满意的，但Delta-bar-Delta算法获得了最好的结果。
Artificial neural networks have gained much attention in recent years as fast and flexible methods for quality control in traditional medicine. Near-infrared (NIR) spectroscopy has become an accepted method for the qualitative and quantitative analyses of traditional Chinese medicine since it is simple, rapid, and non-destructive. The present paper describes a method by which to discriminate official and unofficial rhubarb samples using three layer perceptron neural networks applied to NIR data. Multilayer perceptron neural networks were trained with back propagation, delta-bar-delta and quick propagation algorithms. Results obtained using these methods were all satisfactory, but the best outcomes were obtained with the delta-bar-delta algorithm.