大坝变形预报的模糊神经网络模型

¹ 武汉大学灾害监测与防治研究中心,武汉市珞喻路129号,430079;² 武汉大学测绘学院,武汉市珞喻路129号,430079;³ 湖南省东江水电厂,资兴市,423403;

基金项目: 云南省省院省校合作计划资助项目(2003ZDBLA05A022)；湖北省科技攻关计划重大资助项目(2001AA104A01)；国家自然科学基金资助项目(40474003)

详细信息

Fuzzy Neural Network Modeling for Dam Deformation Prediction

¹ Research Center for Disaster Monitoring and Prevention, Wuhan University,129 Luoyu Road, Wuhan 430079, China ;² School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China ;³ Dongjiang Hydroelectric Power Plant, Hunan Province, Zixing 423403, China

Funds: 云南省省院省校合作计划资助项目(2003ZDBLA05A022)；湖北省科技攻关计划重大资助项目(2001AA104A01)；国家自然科学基金资助项目(40474003)

摘要: 在介绍模糊推理神经网络FNNLM训练算法及网络参数的确定方法的基础上,以东江大坝12个测点的水平位移预报为例,说明了模糊神经网络模型具有训练时间短、预报精度高的优势。
- 模糊神经网络 /
- 大坝变形 /
- 预报模型
Abstract: On the basis of the analysis of characteristics of Fuzzy logic and neural network, the possibility and inevitability of the combination between them are introduced. It gives the FNN-LM algorithm and the method to determine the network parameter. Relative to other models, the superiority of FNN deformation prediction model is showed by the historical displacement prediction models of twelve points of Dongjiang dam for their short training time and high prediction precision.
- fuzzy neural network /
- dam deformation /
- prediction modeling