用径向基函数神经网络模型预报感潮河段洪水位

基金项目: 国家重点基础研究发展规划(973)资助项目(G1999043607);河海大学科技创新基金资助项目(2002)

详细信息

Flood level forecast model for tidal channel based on the radial basis function-artificial neural network

1.
College of Water Resources and Environment, Hohai University, Nanjing 210098, China;
2.
Department of Hydraulic and Hydropower Engineering, Tsinghua University, Beijing 100084, China

Funds: The project is a Key Basic Research Programme of China(G1999043607).

摘要: 径向基函数神经网络方法是一类比较优越的前向式多层神经网络,将其应用于感潮河段的洪水位预报。利用K 均值算法和最小二乘法来确定径向基函数神经网络的参数,并给出了具体计算方法。由于该方法比传统的BP算法有较快的收敛速度,使其具有较大的应用价值。基于感潮河段的具体特点,构建了具有若干个时段预见期的径向基函数神经网络模型。该模型应用于沂河的水位预报,结果表明,该模型运算快速、简便,预报精度较高。
- 感潮河段 /
- 水位预报 /
- 径向基函数 /
- 人工神经网络
Abstract: The radial basis function-artificial neural network(RBF-ANN)is a more excellent neural network,and is applied to flood level forecasting for tidal channel in this paper The parameters of the RBF-ANN are calculated by using the K-mean algorithms and the least square estimation algorithms Compared with the traditional BP algorithm,the RBF-ANN model is fast in convergence,and more valuable in practice Based on character of the tidal channel,the RBF-ANN model with some fore cast lead periods is presented The model is applied to flood level forecasting of Yihe River,and the result shows that the model work is very rapid and the satisfactory results are acquired.
- tidal channel /
- level forecast /
- radial basis function /
- artificial neural network

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