论文标题
基于代谢物组成的丁香芽起源的人工神经网络方法
Artificial Neural Network Approach for the Identification of Clove Buds Origin Based on Metabolites Composition
论文作者
论文摘要
本文研究了人工神经网络方法在基于代谢产物组成的丁香芽的起源中的使用。通常，大数据集对于准确识别至关重要。大型数据集的机器学习导致基于起源的精确识别。但是，由于缺乏代谢产物组成及其高提取成本，丁香芽使用小型数据集。结果表明，带有一个和两个隐藏层的反向传播和弹性传播准确地识别了丁香好友的起源。一个隐藏层的反向传播分别为培训和测试数据集提供99.91％和99.47％。具有两个隐藏层的弹性传播分别为培训和测试数据集提供99.96％和97.89％的精度。
This paper examines the use of artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for accurate identification. Machine learning with large data sets lead to precise identification based on origins. However, clove buds uses small data sets due to lack of metabolites composition and their high cost of extraction. The results show that backpropagation and resilient propagation with one and two hidden layers identifies clove buds origin accurately. The backpropagation with one hidden layer offers 99.91% and 99.47% for training and testing data sets, respectively. The resilient propagation with two hidden layers offers 99.96% and 97.89% accuracy for training and testing data sets, respectively.
