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| Quantitative Analysis Method of Artificial Neural Network Near Infrared Spectroscopy Based on Fast Fourier Transform |
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| DOI：10.3969/j.issn.1004-4957.年份.月份 |
| KeyWord:NIR FT ANN quantitative analysis |
| Author | Institution |
| 李智，王圣毫，郑维平，赵殿瑞 |
沈阳工程学院仿真中心 |
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| Abstract: |
| After some pretreatments to the original spectra and using the frontal N 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. |
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