基于局部均值分解和相关向量机的变形预测

基于改进局部均值分解（LMD）及加权核函数相关向量机（RVM）算法，构建多尺度变形预测新方法。利用LMD将变形数据分解成多个具有物理意义的变形分量，并基于遗传算法优化的RVM对每个变形分量分别进行预测。将各变形分量预测结果进行叠加，最终建立多尺度变形预测方法，并应用于大坝变形预测。实验结果表明，改进LMD-RVM方法的多个精度指标均优于BP神经网络方法、RVM方法和改进EMD-RVM方法，证实了新方法的有效性及可靠性。

Deformation Prediction Based on Local Mean Decomposition and Relevance Vector Machine

A new multi-scale deformation prediction method is proposed based on improved local mean decomposition (LMD) and weighted kernel function relevance vector machine algorithm (RVM). The deformation data is decomposed into several physical deformation components by LMD, and each deformation component is predicted by RVM optimized by genetic algorithm. Finally, a multi-scale deformation prediction method is established and applied to dam deformation prediction by adding the prediction results of each deformation component. The experimental results show that the improved LMD-RVM method is superior to the BP neural network method, RVM method and the improved EMD-RVM method in many precision indexes, which proves the effectiveness and reliability of the new method.