摘要
最小二乘支持向量机作为数据挖掘新方法,对学习样本质量和数量要求低,学习的泛化性更好。采用最小二乘支持向量机对小样本数据LS-SVM s建立油品调合数学模型,对模型进行仿真试验,结果表明采用LS-SVM s建立的模型精确,并具有良好的泛化性能。
Gasoline blending is the most important link of petroleum chemical industry, and it is a complex and nonlinear process, so it is difficult to build its model. Least Square Support Vector Machines （LS-SVMs） is a new algorithm that can be used to modeling with less sample data and the generalization of modeling result is better. The Least Squares Support Vector Machines（LS-SVMs） is used to build the model of gasoline blending based on the small quantity of samples and the result of simulating experiment is satisfactory.
出处
《化工自动化及仪表》
EI
CAS
2006年第3期14-16,21,共4页
Control and Instruments in Chemical Industry
基金
国家自然科学基金资助项目(60474051)
上海市科委重点攻关资助项目(04DZ11008)
关键词
汽油调合
最小二乘支持向量机
建模
gasoline blending
Least Square Support Vector Machines （LS-SVMs）
modeling
