| 基于模糊LS-SVM算法的大坝变形预测模型 |
| |
| 引用本文： | 彭磊,黄张裕,刘胜男,凌晨阳.基于模糊LS-SVM算法的大坝变形预测模型[J].水电自动化与大坝监测,2011,35(1):51-53. |
| |
| 作者姓名： | 彭磊 黄张裕 刘胜男 凌晨阳 |
| |
| 作者单位： | 河海大学地球科学与工程学院,江苏省南京市,210098 |
| |
| 摘 要： | 在大坝工程变形分析和预测方面,研究了一种基于支持向量度的模糊最小二乘支持向量机(LS-SVM)算法,结合具体实例进行对比分析,结果表明模糊LS-SVM模型的预测精度要高于LSSVM模型,且支持向量机(SVM)的稀疏性也优于LS-SVM模型,可以很好地应用于大坝变形监测分析.
|
| 关 键 词： | 大坝变形分析 模糊最小二乘支持向量机 支持向量度 剪切法 变形预测 |
A Study on Dam Deformation Prediction of Based on Fuzzy LS-SVM Algorithm |
| |
| PENG Lei
,
HUANG Zhangyu
,
LIU Shengnan
,
LING Chenyang.A Study on Dam Deformation Prediction of Based on Fuzzy LS-SVM Algorithm[J].HYDROPOWER AUTOMATION AND DAM MONITORING,2011,35(1):51-53. |
| |
| Authors: | PENG Lei HUANG Zhangyu LIU Shengnan LING Chenyang |
| |
| Affiliation: | PENG Lei,HUANG Zhangyu,LIU Shengnan,LING Chenyang (College of Earth Science and Engineering,Hohai University,Nanjing 210098,China) |
| |
| Abstract: | A kind of fuzzy least squares support vector machine(FLS-SVM) algorithm based on the measure of support vector in the analysis and prediction of engineering deformation has been studied by authors.And the results of comparative analysis in a concrete example show that the prediction accuracy of FLS-SVM model is higher than that of LS-SVM model,and the sparseness of FLS-SVM is better than that of LS-SVM model.This shows that FLS-SVM can be applied to dam deformation monitoring and analysis perfectly. |
| |
| Keywords: | deformation analysis of dam fuzzy least squares support vector machine(FLS-SVM) measurement of Support Vector the method of shear deformation prediction |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《水电自动化与大坝监测》浏览原始摘要信息 |
|
