Research on Concentration of Multi-Component Pollution Gas Based on SVM with Kernel Optimized by Rough Set
CHEN Yuan-yuan1,2, ZHANG Ji-long1,2, LI Xiao1,2, TIAN Er-ming1,2, WANG Zhi-bin1,2, LIU Zhi-chao2
1. State Key Laboratory For Electronic Measurement Technology，North University of China，Taiyuan 030051, China 2. Engineering Technology Research Center of Shanxi Province for Opto-Electronic Information and Instrument，North University of China，Taiyuan 030051, China
Abstract：This paper introduced the application of support vector machines (SVM) regression method based on kernel function optimized by the rough set in the infrared spectrum quantitative calculation. According to kernel function with the rough set classification’s method, the spectrum data (characteristic wavelength section) is optimized. The kernel function leads support vector machines, and the SVM project the two-dimensional room to the multi-dimensional room, and calculate the concentration of every kind of gas in multi-component pollution gas. By using two kinds of typical spectrum data processing algorithm to make the contrast, the comparison of five kinds of gaseous mixture various proximate analysis is carried out, and when the spectrum separable rate is high, the predicted values of the three methods approach the normal value, and the average error is smaller than 0.13; but when the spectrum separable rate is low, the RS-SVM predicted value is more precise than the first two kinds. Experimental data show that the consequence is better when there are more testing types, and the precision and operation of this method is of more remarkable superiority.
陈媛媛1，2，张记龙1，2，李 晓1，2，田二明1，2，王志斌1，2，刘智超2 . 基于粗糙集核优化的支持向量机在多组分污染气体定量分析中的研究与应用 [J]. 光谱学与光谱分析, 2010, 30(12): 3384-3387.
CHEN Yuan-yuan1,2, ZHANG Ji-long1,2, LI Xiao1,2, TIAN Er-ming1,2, WANG Zhi-bin1,2, LIU Zhi-chao2 . Research on Concentration of Multi-Component Pollution Gas Based on SVM with Kernel Optimized by Rough Set . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(12): 3384-3387.
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