水利水电技术2017，Vol.48Issue(3)：104-108,5.DOI:10.13928/j.cnki.wrahe.2017.03.019
基于人工蜂群算法与Elman神经网络的大坝变形监控模型
Artificial bee colony algorithm and Elman neural network-based model for dam deformation monitoring
摘要
Abstract
Aiming at the problems from the Elman neural network,such as slow convergence rate,prone to fall into local minimum,etc.,an artificial bee colony algorithm and Elman neural network combined dam deformation monitoring model is established.The result from the application of it to a gravity dam shows that both the relative error and the standard error of the deformation prediction from the only Elman neural network modeling are 3.50％ and 0.131resepctively,while both the relative error and the standard error of the deformation prediction from ABC-Elman (artificial bee colony algorithm and Elman neural network) model are 1.98％ and 0.063 respectively.From the aspects of the contributions from all the factors of impacts on dam deformation,the component of water pressure is 27.9％,the component of temperature is 62.3％ and the component of time-effectiveness is 9.8％.Generally,the ABC-Elman model has a certain advantage in the aspects of modeling efficiency and prediction accuracy,thus is not only suitable for modeling analysis on dam deformation,but also can be popularized to the models for monitoring seepage,stresses,etc.关键词
大坝变形/监控模型/Elman神经网络/人工蜂群算法/金沙江水电基地/云南省昭通市水富县/大坝安全Key words
dam deformation/monitoring model/Elman neural network/artificial bee colony algorithm/Jinsha River hydropower base/Yunnan Shuifu County/dam safety分类
水利科学引用本文复制引用
李鹏鹏,苏怀智,郭芝韵,钱秋培..基于人工蜂群算法与Elman神经网络的大坝变形监控模型[J].水利水电技术,2017,48(3):104-108,5.基金项目
国家自然科学基金(51579083,51479054,41323001) （51579083,51479054,41323001）
