基于多核模式的隧道沉降预测

为提高支持向量机的预测精度，提出一种基于自适应多核学习模型的预测方法。自适应多核学习算法中采用树形筛选结构，通过生枝、剪枝操作完成核函数间的相加、相乘处理，增强了多核函数的非线性与多样性。采用网格遍历和粒子群算法对核参数、权重系数及模型参数进行寻优处理，弥补了训练样本缺少先验知识对参数赋值产生的偏差。将多核学习方法用于地铁隧道沉降预测，并与单核函数的预测数值进行对比。试验结果表明，自适应多核学习模型有效提高了预测模型的预测精度及泛化性能。

Settlement prediction of tunnel based on multiple kernels learning mode

To improve the prediction precise of support vector machine model, an adaptive multiple kernels learning (AMKL) method is proposed. In this method, a tree structure is used to screen the kernels. Additionally, this processing can be implemented with growing and cutting branches manipulation for adding and multiplying the kernels in each layer. This would enhance the nonlinear and diversity characteristics of multi-kernels. Grid traversal and particle swarm optimization method are applied to solve the optimization problem of kernel parameters, weight coefficient and model parameters. It could offset the assignment deviation of parameters which occurs in the lack of a prior knowledge of the training samples. AMKL method is used to predict the settlement of metro tunnel. Comparisons between experimental results of AMKL and the ones of single kernel functions show that AMKL effectively improves the accuracy and generalization.