福州大学学报（自然科学版）2011，Vol.39Issue(3)：438-442,5.DOI:CNKI:35-1117/N.20110526.1115.003
基于经验模式分解与LM一BP神经网络的大坝变形预报模型
Dam deformation prediction model based on empirical mode decomposition and LM- BP neural network
摘要
Abstract
A novel model based on empirical mode decomposition ( EMD) and neural network for dam deformation prediction is presented in the paper. Firstly, considering that EMD has an advantage to do adaptive decomposition according to characteristics of the signal itself, deformation time series is decomposed into a series of intrinsic mode functions (IMF) in different scale space. Then, according to the change regulation of each IMF, they are forecasted by appropriate LM - BP neural networks. Finally, these forecasting results of each IMF are combined to obtain final forecasting result. The calculation result of a practical example shows that this model has higher forecasting precision and better adaptability.关键词
经验模式分解/神经网络/大坝/变形/预报Key words
empirical mode decomposition/ neural network/ dam/ deformation/ prediction分类
天文与地球科学引用本文复制引用
范千,许承权,方绪华..基于经验模式分解与LM一BP神经网络的大坝变形预报模型[J].福州大学学报（自然科学版）,2011,39(3):438-442,5.基金项目
福建省教育厅科研资助项目(JA10045) （JA10045）
江西省数字国土重点实验室开放基金资助项目(DLLJ201102) （DLLJ201102）
福建省自然科学基金资助项目(2009J05102) （2009J05102）
福州大学科研启动基金资助项目(022355) （022355）
