基于正交设计下SVM滑坡变形时序回归预测的超参数选择

超参数的选择直接影响着支持向量机（SVM）的泛化性能和回归效验，是确保SVM优秀性能的关键。针对超参数穷举搜索方法的难点，从试验设计的角度，提出了正交设计超参选择方法，并分析了基于混合核函数（比单一核函数具有更好的收敛性和模型适应性）SVM各个超参数的取值范围，选定了每个参数的试验水平。通过考虑参数间的正交性和交互性，选取最优超参数组合下的SVM模型。应用该方法，对两种典型滑坡位移时序的SVM建模进行了超参数组合正交优化设计，获得了精度高且泛化性能良好的滑坡预测模型，其试验结果验证了方法的可靠性。正交设计超参选择方法较之其他超参选择法简单实用，其高时效的特点更有助于SVM在实践工程中的良好应用。

On choice of hyper-parameters of support vector machines for time series regression and prediction with orthogonal design

Selection of the hyper-parameters is critical to the performance of support vector machines (SVM), directly impacting the generalization and regression efficacy of the SVM. An orthogonal experimental design procedure for hyper-parameter selection (ODPS) is clearly desirable given the intractable problem of exhaustive search methods. The authors' previous work in this area involved analyzing the range value of hyper-parameters for SVM of mixed kernel which has been proved and showed a higher convergence rate and a greater flexibility in learning a problem space than single kernel functions, and determining experimental levels for different parameters in order to guide the hyper-parameter selection process. The method selects hyper-parameters optimal composition in terms of orthogonal and interaction effect of hyper-parameters. The results of the performed engineering experiments for the prediction of two typical landslide deformation time series confirmed the reliability and advantage of the proposed approach.