关键词: 软测量 , 最小二乘支持向量机 , 双核函数 , 干点

Abstract: According to the function of mononuclear least squares support vector machine is easy to fall into local optimal value, the dualcore kernel function least squares support vector machine model method was proposed. This method uses Sigmoid kernel function and RBF kernel function which was linear weighted composing dualcore function, Removing the part of the smaller support vector sample method for improving the least squares support vector machine sparse characteristic, improving model operation speed, cross validation method was used to optimize the part of parameters. Finally the method was used to establish the gasoline dry point soft measurement model, compared with the standard support vector machine and the function of mononuclear least squares support vector machine. The results show that dualcore function least squares support vector machine model has higher calculation accuracy and better generalization ability.

Key words: Soft sensing,    ,  The least squares support vector machine,    ,  Dualcore function,   ,  Dry point 