Simultaneous Determination of Multicomponents in Air Toxic Organic Compounds Using Artificial Neural Networks in Ftir Spectroscopy
作者:
Yan Li,
Shulin Yang,
Junde Wang,
Binghe Gu,
Fang Liu,
期刊:
Spectroscopy Letters
(Taylor Available online 1999)
卷期:
Volume 32,
issue 3
页码: 421-429
ISSN:0038-7010
年代: 1999
DOI:10.1080/00387019909349995
出版商: Taylor & Francis Group
关键词: Multicomponents Analysis;Organic Compounds Analysis;Artificial Neural Networks;FTIR
数据来源: Taylor
摘要:
The application of Artificial Neural Networks (ANNs) for nonlinear multivariate calibration using simulated FTIR data was demonstrated in this paper. Neural networks consisting of three layers of nodes were trained by using the back-propagation learning rule. Since parameters affect the performance of the network greatly, simulated data were used to train the network in order to get a satisfactory combination of all parameters. The mixtures of four air toxic organic compounds whose FTIR spectra are overlapped were chosen to evaluate the calibration and prediction ability of the network. The relative standard error (RSD%), the percent standard error of prediction samples (%SEP) and the percent standard error of calibration samples (%SEC) are used for evaluating the ability of the neural network.
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