The Quantitative Analysis of Polycomponent PAHs by Netural Network Based on Data Synthese and Principal
QU Wei-wei1,2, SHANG Li-ping2*, LI Xiao-xia2, LIU Jing2
1. Network Information Center, Southwest University of Science and Technology, Mianyang 621010, China 2. College of Information Engineer, Southwest University of Science and Technology, Mianyang 621010, China
Abstract：The present paper used synthesized data from the experiment samples to replace partial basic experiments, and increased the training samples amount from 14 to 27. In principal component analysis (PCA), the dimensionality of multivariate data was reduced to n principal components and almost all data information was kept. The PCA reduced the network’s input nodes from 60 to 3 to simplify the neural network’s structure. Finally, back-propagation neural network was used to train and predict these samples. It had 27 training samples, the input layer had three nodes, the hidden layer had two nodes, and the output layer had two nodes. Its excitation function is variable learning rate method. The results show that the coefficient of recovery can reach 89.6-109.0. It has reached the expected purpose.
屈薇薇1，2，尚丽平2*，李晓霞2，刘 晶2 . 基于数据拟合和主成分分析的多组分PAHs神经网络定量分析 [J]. 光谱学与光谱分析, 2010, 30(10): 2780-2783.
QU Wei-wei1,2, SHANG Li-ping2*, LI Xiao-xia2, LIU Jing2 . The Quantitative Analysis of Polycomponent PAHs by Netural Network Based on Data Synthese and Principal . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(10): 2780-2783.
