摘要
采用主成分分析进行样本集特征的提取,结合支持向量机建立回归模型,并对成品卷烟主流烟气中的总粒相物、焦油量和烟气烟碱含量进行定量预测。结果表明:总粒相物、焦油和烟气烟碱的预测均方差分别为0.61,0.47和0.04,与模型相比分别下降了30.73%,26.12%和8.15%,体现了更高的预测准确度。
A new quantitative regression model was built in combination with extracts feature from sample cluster using principal component analysis（PCA）.A nonlinear model was built using support vector regression（SVR） to predict total particulate matter,tar and nicotine content in cigarette smoke.Results showed that the RMSEP（root mean square error of prediction） of total particulate matter,tar and nicotine in smoke was 0.61,0.47 and 0.04 respectively,reduced by 30.73%,26.12% and 8.15% compared with SVM method,indicating that PCA-SVM method resulted in high prediction accuracy.
出处
《中国烟草学报》
EI
CAS
CSCD
2010年第6期21-24,共4页
Acta Tabacaria Sinica
关键词
主成分分析
支持向量机
总粒相物
焦油
烟气烟碱
principal component analysis
support vector regression
total particulate matter
tar
nicotine
