支持向量机算法在CO浓度传感器中的应用

针对基于红外光谱的CO气体定量分析模型对机动车尾气排放中有害气体CO的定量分析；选取了浓度范围在0.5%～20%的15组不同浓度的CO气体样本，建立CO浓度的支持向量机（ SVM）回归分析模型，基于改进的网格搜索法对SVM的相关参数进行了优化。实验结果表明：经过SVM的回归分析，与传统的光谱吸收方法相比，处理后浓度值比实验所得浓度值更接近CO标定值；与粒子群优化（ PSO）算法作对比，采用网格搜索法获得的最佳参数糟＝0.707，早＝0.5，PSO获得的糟＝55.911，早＝0.01，所用时间比PSO算法节省约40%。SVM应用于CO的浓度分析，符合实验要求，回归效率提高。

Application of SVM algorithm in CO concentration sensor

Quantitative analysis experiment is performed based on IR CO gas quantitative analysis model for harmful gases CO emitted by vehicle. Model is set concentration ranges of 0. 5~20%,15 groups of samples are selected. Support vector machine( SVM)are used to build the regression model for sample data. Related parameters are optimized based on the method of improved grid search. The experimental results show that after regression analysis by SVM,compared to the traditional method of spectral absorption,regression sample significantly closer to the calibration concentrations than the experimental values. And compared with PSO algorithm,this method obtains c= 0.707,g=0. 5,PSO obtains c=55.911,g=0. 01,the time of modeling by improved grid search is reduced about 40 % of PSO algorithm. SVM used in CO concentration analysis is coincide with the test requirements and regression efficiency is high.