基于LS-SVM的橡胶炭黑均匀度判定模型

为了减少橡胶生产过程中的废品率，需要间接、实时、准确地确定橡胶中炭黑的分散均匀度。基于结构风险最小化原则的支持向量机(SVM)是一种新型的机器学习方法，对于小样本决策具有良好的分类、推广能力．应用多元分类的最小二乘支持向量机建立了橡胶炭黑分散均匀度六级判定模型，并对实际数据进行判定，平均错别率下降到3．6％。结果表明该模型是可行的，并能快速、准确地判定橡胶炭黑的分散均匀度。

Model to Determine Dispersed Homogeneous Degree of Carbon-black in Rubber Based on LS-SVM

In order to decrease the rejection ratio in the rubber-producing process, the dispersed homogeneous degree of carbon-black in rubber should be indirectly, real time and accurately determined. Support Vector Machines based on the principle of structural risk minimization is a new machine learning method which is of excellent classification and generalization ability when being used in the small sample decision. In this paper, a 6-band determination model of dispersed homogeneous degree of carbon-black in rubber was established on the basis of multi-class Least Squares Support Vector Machines, and some practical data were determined by the model. The results show that the proposed model is practical and average rate of false determination reduce to 3.6%, and can determine the dispersed homogeneous degree of carbon-black in rubber quickly and correctly.