水利水电技术2017，Vol.48Issue(12)：41-44,62,5.DOI:10.13928/j.cnki.wrahe.2017.12.007
基于EMD分解法的大坝变形预测模型及应用
EMD decomposition method-based dam deformation prediction model and its application
摘要
Abstract
Aiming at the situation of that the prediction effect from the conventional model on the fluctuation time series is poor,a EMD decomposition method-based dam deformation prediction model is put forward herein in combination with the merits of the empirical mode decomposition (EMD) and the theory of the relevance vector machine (RVM) as well as the improved particle swarm optimization (IPSO) algorithm.At first,the dam deformation time series is decomposed and reconstructed with the EMD decomposition method to make the non-stationary dam deformation time series stationarized,and then the prediction is carried out based on the RVM theory,for which the gauss function is used as the kernel function and the improved particle swarm optimization (IPSO) algorithm is adopted for the optimization,thus the EMD-RVM (IPSO) prediction model for dam deformation is established finally.It is obtained through the relevant actual case that the mean residuals of the SVM,RVM and EMD-RVM (IPSO)models are 5.29 mm,3.13 mm and 0.97 mm respectively,while all the errors of the predicted values from the EMD-RVM(IP-SO) model are controlled within the range of 5％.It is demonstrated that the pre-processing of the non-stationary time series made by the EMD decomposition method can effectively enhance the prediction accuracy,thus if compared with the standardized SVM model and RVM model,the prediction accuracy of the EMD-RVM(IPSO) is higher with better structure sparsity,and then has a certain feasibility in the engineering practice concerned.关键词
大坝变形/预测/EMD分解法/相关向量机/改进粒子群算法Key words
dam deformation/prediction/EMD decomposition method/relevance vector machine/improved particle swarm optimization algorithm分类
建筑与水利引用本文复制引用
金盛杰,包腾飞,陈迪辉,钱秋培..基于EMD分解法的大坝变形预测模型及应用[J].水利水电技术,2017,48(12):41-44,62,5.基金项目
国家自然科学基金面上项目(51479054) （51479054）
国家自然科学基金项目(51579085) （51579085）
国家自然科学基金项目(41323001) （41323001）
江苏省2015年度普通高校研究生科研创新计划项目(KYZZ15-0140,KYZZ15-0138) （KYZZ15-0140,KYZZ15-0138）
