针对滑坡位移时间序列的非线性特性,引入基于相空间重构和最小二乘支持向量机(LSSVM)的预测法。利用Cao氏法确定嵌入维数,计算最佳延迟时间;在相空间中,利用LSSVM建立预测模型,以实例对滑坡进行计算,对LSSVM模型和BP神经网络模型进行了比较。结果表明:基于相空间重构和LSSVM的滑坡预测模型具有较高的精度,是科学可行的。 更多还原
【Abstract】 In view of the nonlinear characteristics of landslide displacement time sequence,this paper introduced the prediction method based on phase space reconstruction and least squares support vector machine(LSSVM).Used Cao’s method to determine the embedding dimension,according to mutual information method to compute the best delay time;Then in the phase space,used least squares support vector machine(LSSVM) to establish the forecast model to compared with LSSVM and the neural network predicting mode... 更多还原