CAJ | 학술논문

基于组合预测思想，结合 BP 神经网络和马尔科夫链2种预测方法，构建了一种新维 BP 神经网络－马尔科夫链大坝沉降预测模型。通过对训练样本的学习，利用新维改进的 BP 神经网络算法实现了对沉降位移时间序列的滚动预测。在此基础上，借助马尔科夫链模型对其随机扰动误差进行修正，有效地提高了预测结果的精度。将构建的组合模型应用于长洲大坝船闸控制楼沉降位移时序预测中，研究结果表明该模型预测精度较高、可靠性好，提高了模型的中长期预测能力，为大坝沉降预测提供了一种有效的新方法。
기우조합예측사상，결합 BP 신경망락화마이과부련2충예측방법，구건료일충신유 BP 신경망락－마이과부련대패침강예측모형。통과대훈련양본적학습，이용신유개진적 BP 신경망락산법실현료대침강위이시간서렬적곤동예측。재차기출상，차조마이과부련모형대기수궤우동오차진행수정，유효지제고료예측결과적정도。장구건적조합모형응용우장주대패선갑공제루침강위이시서예측중，연구결과표명해모형예측정도교고、가고성호，제고료모형적중장기예측능력，위대패침강예측제공료일충유효적신방법。
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.