摘要
为提高近红外光谱法检测汽油辛烷值的精度,该文提出一种汽油辛烷值近红外光谱检测的改进极限学习机(i ELM)新型建模方法。该算法融合了极限学习机算法(ELM)与基于变量投影重要性系数的改进叠加偏最小二乘回归(VIP-SPLS)模型算法,有效解决了ELM模型隐含层输出矩阵维数高和高度共线性的问题。采用该算法对汽油辛烷值的近红外光谱检测数据进行建模,发现改进极限学习机模型的精度比现有的偏最小二乘回归模型和极限学习机模型分别提高20.0%和29.3%,验证了方法的有效性。实验表明,该文方法可用于汽油辛烷值的近红外光谱检测,检测精度良好。
In order to improve the accuracy of gasoline octane number detection by the near infrared （NIR） spectroscopy, an improved extreme learning machine （ iELM ） algorithm combined with theextreme learning machine（ ELM） algorithm and the improved stacked partial least square regression based on the variable importance in the projection （ VIP-SPLS） algorithm is proposed here. And it solves the problem of high dimension and high collinearity in the output matrix of hidden layer of the ELM algorithm effectively. Then the proposed method is applied to a commonly used benchmark NIR spectral data of gasoline octane number detection. The results show that, compared with the PLS model and the ELM model, the accuracy of iELM model is increased by 20. 0 % and 29. 3 % respectively. The experiment shows that the iELM algorithm can be applied to the gasoline octane number detection by the near infrared spectroscopy and its accuracy is satisfactory.
出处
《南京理工大学学报》
EI
CAS
CSCD
北大核心
2017年第5期660-665,共6页
Journal of Nanjing University of Science and Technology
基金
国家自然科学基金(61590925
U1509211)
关键词
汽油辛烷值
近红外光谱
模型
极限学习机
偏最小二乘
变量投影重要性系数
gasoline octane number
near infrared spectroscopy
models
extreme learning machine
partial least square
variable importance in the projection
作者简介
胡碧霞（1994-）,女,博士生,主要研究方向：工业大数据分析,E-mail：hubx@zju.edu.cn;
通讯作者：卢建刚（1968-）,男,博士,教授,博士生导师,主要研究方向：复杂工程系统的智能感知、建模、控制与优化,E-mail：lujg@zju.edu.cn.
