基于MI-LSSVM的水泥生料细度软测量建模

针对水泥生料细度软测量模型难以建立的问题,考虑到输入变量选择易受时延的影响,提出一种基于互信息和最小二乘支持向量机(MI-LSSVM)的软测量建模方法。该方法采用互信息表征变量间的相关性,进而解决水泥生料细度软测量建模中的时延问题,并在此基础之上,提出双向选择算法获取输入变量,将得到的输入变量应用于最小二乘支持向量机中,建立水泥生料细度软测量模型,最后应用水泥厂的实际数据对基于互信息和最小二乘支持向量机的水泥生料细度软测量模型进行仿真。结果表明该方法预测精度高、泛化能力强。

Soft sensor modeling for cement fineness based on least squares support vector machine and mutual information

To accurately establish the soft sensor model of cement fineness, the selection of input variables is easily influenced by time delay. Thus, this paper proposes a soft sensor modeling method based on mutual information and least square support vector machine (MI LSSVM). In the proposed method, the correlation of variables is represented by mutual information, which can be used to determine the time delay of each auxiliary variable. Furthermore, the two way selection algorithm is proposed to obtain input variables, and a soft sensor model of cement fineness is built based on least squares support vector machine with these selected input variables. Finally, the proposed model is trained with actual operational data of a cement plant. The experimental results show that the model can achieve high precision and generalization.