基于支持向量机的沥青路面使用性能预测探究

Studied on Performance Prediction of Asphalt Pavement Based on Support Vector Machine

在考虑模型的二次损失函数下,对传统SVM进行扩展开进,得到基于最小二乘支持向量机(LS—SV M)的回归模型,利用支持向量机(SVM)具有的泛华能力,通过非线性映射构建一个高维空间的最优超平面,来进行评价计算。通过对选取的样本的归一化处理,确定LS—SVM输入向量数量指标,引入拉格拉日函数进行变化,获得函数的对偶关系,从而在保证LS—SVM的训练速度和预测精度,快速寻找到模型的解。最后通过算例验证了模型在拟合过程和预测经过精度方相较于其他模型具有明显的优势,能有效满足实际工程应用需求。

In this paper,under considering the model of quadratic loss function,have extended into the traditional SVM,based on least squares support vector machines(LS-SV M)of the regression model,using support vector machine(SVM)has the ability of shi by nonlinear mapping to build a high dimensional space to the optimal hyperplane,calculated to evaluate.By the normalization processing of samples selected,determine the LS-SVM input vector quantitative index,introducing the gulag,function change,dual relationship of the function,to ensure that the LS-SVM training speed and precision.Quickly find the solution to the model.Finally,an example is given to verify that the model has obvious advantages compared with other models in fitting process and prediction,and can effectively meet the application demand of engineering.