长江科学院院报Issue(10)：23-27,32,6.DOI:10.11988/ckyyb.20140310
基于新维 BP 神经网络－马尔科夫链模型的大坝沉降预测
Prediction of Dam Settlement Using Metabolism BP Neural Network and Markov Chain
万臣 1李建峰 2赵勇 1张金龙3
作者信息
- 1. 长安大学 建筑工程学院，西安 710064
- 2. 武警水电三总队 八支队，成都 401347
- 3. 武警水电三总队 八支队，成都 401347
- 折叠
摘要
Abstract
A dam settlement prediction model integrating BP neural network model and Markov chain prediction was built in this paper.Through emulating the training samples,rolling prediction for the settlement displacement time series was performed by the metabolism-improved BP neural network algorithm.Furthermore,Markov chain was used to correct its random disturbance and the prediction results were improved.This model was applied to the set-tlement displacement timing prediction of Changzhou dam lock control building.The result shows that the model has high prediction accuracy and good reliability.It improves the long-term prediction ability,and provides an effective method for dam settlement prediction.关键词
沉降预测/BP 神经网络/马尔科夫链/大坝监测/长洲水利枢纽Key words
settlement prediction/BP neural network/Markov chain/dam monitoring/Changzhou water power junc-tion分类
建筑与水利引用本文复制引用
万臣,李建峰,赵勇,张金龙..基于新维 BP 神经网络－马尔科夫链模型的大坝沉降预测[J].长江科学院院报,2015,(10):23-27,32,6.
