摘要
针对大坝工作条件复杂,影响因素繁多,致使现有监控模型预报精度偏差过大问题,基于递阶对角神经网络能够逼近任意非线性函数的特点,使用串并联模型辨识器,采用动态BP学习算法,以水压、温度和时效因子为输入量,坝体位移为输出量,结合工程实例提出了大坝变形监测的递阶对角神经网络模型,并将该模型用于坝体变形数据的拟合分析及其预测预报.研究表明,该网络不仅收敛速度快,提高了算法的效率,而且对实测数据具有较好的拟合效果,提高了预报精度,在大坝安全预测分析中具有有效性和优越性.
In order to deal with dam monitoring data more effectively, a new deformation monitoring model has been proposed based on hierarchical diagonal neural network （HDHH） that can approximate any nonlinear function. The model takes water pressure, temperature and time factors as the input and dam displacement as the output. And then the series-parallel model identifier and dynamic BP learning algorithm play an important role in modeling. The dam deformation data fitting analysis and forecasting research show that the HDNN model is not only convergent quickly enough to improve the efficiency of the algo-rithm, but also has a good effect in fitting with monitoring data ,which improves forecast accuracy a lot. In a word, HDNN has great validity and superiority in the dam safety forecast analysis.
出处
《武汉大学学报（工学版）》
CAS
CSCD
北大核心
2009年第3期344-348,共5页
Engineering Journal of Wuhan University
基金
国家自然科学基金项目(编号:50539030
50539110
50539010
50809025
50879024)
国家科技支撑计划课题(编号:2006BAC14B03
20070294023
2008BAB29B03
2008BAB29B06)
中国水电工程顾问集团公司科技项目(编号:CHC-KJ-2007-02)
江苏省"333高层次人才培养工程"科研项目(编号:2017-B08037)
关键词
大坝变形预测
递阶对角神经网络
串并联模型
动态BP学习算法
预报
dam deformation
hierarchical diagonal neural network
series-parallel model
dynamic BP learning algorithm
forecast
