| Autor: |
Tang XJ; State Key Laboratory of Electrical Insulation for Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China. xiaojun_tang@mail.xjtu.edu.cn, Hao HM, Li YJ, Liu JH |
| Jazyk: |
čínština |
| Zdroj: |
Guang pu xue yu guang pu fen xi = Guang pu [Guang Pu Xue Yu Guang Pu Fen Xi] 2011 Jun; Vol. 31 (6), pp. 1673-7. |
| Abstrakt: |
Feature variable selection and modeling are two of the most principal research contents in spectral analysis. In the present paper, beginning from the introduction of feature spectrum selection based on Tikhonov regularization and discussion on it's application in multi-component mixed alkane gas analysis, 7 sets of feature spectra were abstracted from the absorption spectra of 7 kinds of alkane gas, including methane, ethane, propane, iso-butane, n-butane, iso-pentane and n-pentane. In order to overcome the problem of over-training of neural network, a method called optimal parameter selection of neural netework (NN) was presented to build analysis model of analyte. Optimal parameters were selected from many trained networks with same architecture based on error process. And analysis models of spectral analysis for 7 kinds of alkane gas were built. Finally, the testing analysis results done with standard gases are given. The results show that the method presented in this paper can be used to reduce the cross--sensitivity between any two kinds of gas. The cross-sensitivity is less than 0.5%. The resolving power is as high as 20 X 10(-6). |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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