传感技术学报2016，Vol.29Issue(9)：1464-1470,7.DOI:10.3969/j.issn.1004-1699.2016.09.028
基于自适应变异粒子群算法的混合核ε-SVM在混合气体定量分析中的应用
Application of Mixed Kernel Functionε-SVM Based on Adaptive Mutation Particle Swarm Optimization in Multi Component Gas Detection
摘要
Abstract
Due to the simultaneous measurements of automobile exhaust gas by using the multi-component gases sensor based on the dispersion of light infrared method(NDIR),the text is the result of the cross absorption and in⁃terference,resulting in the large measurement error and low accuracy. To solve this problem,a kind of mixed kernel functionε-SVM based on adaptive mutation particle swarm optimization algorithm is put forword to establish a mod⁃el for the quantitative analysis of three component mixture gases. Collect the concentration signals of CO 2,CO and C3H8 as the model inputs,through the model regression analysis,the outputs are corresponding mixed gases concen⁃trations. thus,the problem of mutual interference can be solved. Finally,the performance of the model is analyzed through the experimental data,the result shows that the average error of the model is significantly reduced com⁃pared to the traditional model.关键词
检测技术与自动化装置/气体定量分析/自适应变异粒子群算法/混合核函数/支持向量机/气体传感器Key words
detection technique and automatic device/gas quantitative analysis/mixed kernel function/adaptive mutation particle swarm optimization algorithm/SVM分类
信息技术与安全科学引用本文复制引用
刘文贞,陈红岩,李孝禄,袁月峰,郭晶晶..基于自适应变异粒子群算法的混合核ε-SVM在混合气体定量分析中的应用[J].传感技术学报,2016,29(9):1464-1470,7.基金项目
中国计量大学第十九届学生科研计划项目（院级） （）
