基于LMD与样本熵的多尺度大坝变形预测

针对大坝变形数据的多尺度特征，将局域均值分解、样本熵及高斯过程算法应用于大坝变形预测中，提出了多尺度大坝变形预测新模型。首先利用局域均值分解算法对变形数据进行多尺度分析，挖掘变形数据隐含的信息，随后根据各变形分量特征，构建基于高斯过程的多尺度大坝变形预测模型，并利用样本熵对模型进行简化。通过实例分析，证实该大坝变形预测新方法精度高于BP网络和最小二乘支持向量机模型。

Prediction of dam deformation based on local mean decomposition and sample entropy

Aiming at the multi-scale characteristics of dam deformation data, a new model for multi-scale deformation forecasting was proposed based on the local mean decomposition, sample entropy and Gaussian process algorithm. Firstly, the deformation data are analyzed by the local mean decomposition algorithm to find out the implicit information; then the multi-scale prediction model for dam deformation is built based on Gaussian process according to the characteristics of each deformation component, and the model is simplified by sample entropy. The experimental results prove that the new prediction method is better than BP and SVM model in accuracy.