分析测试学报2012，Vol.31Issue(3)：343-346,4.DOI:10.3969/j.issn.1004-4957.2012.03.019
基于傅立叶变换的人工神经网络近红外光谱定量分析法
Quantitative Analysis Method of Artificial Neural Network Near Infrared Spectroscopy Based on Fast Fourier Transform
摘要
Abstract
After some pretreatments to the original spectra and using the frontal TV coefficients of the fast Fourier transform (FFT) as the input variables of the artificial neural network (ANN) , a lot of useful information was assured to participate in modeling, and the advanced filter function of the FFT was also realized. After modeling the octane in gasoline and the calorific value in coal powder, the FFT - RBF(the radial basis function network) model was found to be good, for example, when using the frontal 20 coefficients of FFT, the root mean square error(RMSEP) of prediction of the octane is 0. 152, and its correlation coefficient is 0. 976, and when using the frontal 30 coefficients of FFT, the RMSEP of the Qgr. D of the coal powder is 0. 256, and its correlation coefficient is 0. 923 . The research illustrated that the ANN NIR quantitative analysis method based on the FFT, especially the FFT - RBF had the tremendous advantage in NIR prediction function.关键词
近红外光谱/傅立叶变换/人工神经网络/定量分析Key words
NIR/ FT/ ANN/ quantitative analysis分类
化学化工引用本文复制引用
李智,王圣毫,郑维平,赵殿瑞..基于傅立叶变换的人工神经网络近红外光谱定量分析法[J].分析测试学报,2012,31(3):343-346,4.基金项目
沈阳市科技攻关项目(1071122-2-00) （1071122-2-00）
