Abstract：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 20×10-6.
Key words：Multi-component gas quantitative analysis;Feature spectra selection;Tikhonov regularization;Cross-sensitivity;Neural network
汤晓君，郝惠敏，李玉军，刘君华. 基于Tikhonov正则化特征光谱选择与最优网络参数选择的轻烷烃气体分析[J]. 光谱学与光谱分析, 2011, 31(06): 1673-1677.
TANG Xiao-jun, HAO Hui-min, LI Yu-jun, LIU Jun-hua. Analysis of Mixed Alkane Gas Based on Tikhonov Regularization Spectra Selection and Optimal Neural Network Parameters Selection . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(06): 1673-1677.
