Application of Improved BP Neural Network Model in Uplift Pressure Monitoring

被引：0

作者：

Mei Yi-tao ^{[1
]}

Zheng Dong-jian ^{[1
]}

Xu Lei ^{[1
]}

机构：

[1] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Jiangsu, Peoples R China

来源：

MULTI-FUNCTIONAL MATERIALS AND STRUCTURES ENGINEERING, ICMMSE 2011 | 2011年 / 304卷

关键词：

Uplift Pressure Monitoring; BP Neural Network; Network structure; Initial parameters; Optimization;

D O I：

10.4028/www.scientific.net/AMR.304.24

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The network structure, initial weights and initial thresholds were optimized to solve some problems, such as over-fitting and slow convergence rate in standard BP Neural Network. Combining the base seepage character of concrete dam, a uplift pressure monitoring model is established in this paper with measured data of a actual concrete dam. The advantage of the presented model is tested and validated by actual examples. It has positive significance in the actual application.

引用

页码：24 / 30

页数：7