滑坡位移时序预测的核函数构造

获得支持向量机(SVM)背景下滑坡位移时序准确预测的关键，是构造或选择一合适的核函数。通过分析滑坡位移时序曲线特征以及不同类型Mercer核的性质，从基于核函数上的封闭运算角度，构造出支持向量机背景下预测滑坡位移时序的最佳核函数。利用3组不同特征的滑坡位移时序，对构造出的核函数进行性能检验，数值实验表明：对于典型的3组滑坡时序，LPG与MPG核的学习性能要优于简单核，且前者适合复杂位移时序的回归预测，而后者更适合规律性较强的简单时序曲线的建模预测。此外，探讨了这两种核函数下的核参数取值对模型精度的影响。

Kernel design for displacement time series of landslide

The successful key, the prediction of displacement time series of landslide based on support vector machines (SVM), is designing or choosing a appropriate kernel function. The characters of classical displacement time series of landslide are analyzed; and some general properties of Mercer kernels are discussed. And we use a number of closure properties to create optimal kernels for the SVM prediction of landslide displacement data. The efficiencies of designed kernels are tested by predicting three groups of displacement time series. All experimental results show LPG and MPG kernels outperform the standard kernels, and the LPG kernel is of efficient about the regression and prediction of complex displacement time series. The MPG kernel is appropriate for simple deformation datasets with obvious laws. Furthermore, some good experiences are obtained about kernel parameters choice by investigating interconnection between different parameters and models learn performance.