Analysis of Mixed Alkane Gas Based on Tikhonov Regularization Spectra Selection and Optimal Neural Network Parameters Selection

作者：Tang Xiao-jun^*; Hao Hui-min; Li Yu-jun; Liu Jun-hua

来源：Spectroscopy and Spectral Analysis, 2011, 31(6): 1673-1677.

DOI：10.3964/j.issn.1000-0593(2011)06-1673-05

• Multi-component gas quantitative analysis
• Feature spectra selection
• Tikhonov regularization
• Cross-sensitivity
• Neural network

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).

• 出版日期2011-6
• 单位西安交通大学