大坝基础渗流量预测的交替学习人工神经网络方法研究

人工神经网络通过神经元之间的相互作用来完成整个网络的信息处理，具有高度非线性、自适应、自学习等一系列优点，广泛应用于物理量的预测预报中。为此提出并建立了基于交替学习迭代算法的人工神经网络模型，结合清江隔河岩水电站的实际，研究了这种模型在大坝基础渗流量预测中的应用，其预报精度较高，可望推广应用到大坝安全监控中去。

Prediction of Seepage Quantities of a Dam Foundation Based on Artificial Neural Network Model with Learning into Groups

In artificial neural network(ANN)method,the information treatment of the network are finished through interaction of neurones of the network There are a series of advantages in the methodology,such as high degree non linear ,self adaptation,self learning,etc..Therefore the ANN method is used widely in the fields of predictions of physical quantities This paper presents and establishes an ANN model based on the training method of learning into groups Combining the practice of Geheyan Hydropower project,application of the ANN model to prediction of seepage quantities of the dam foundation is studied There are high degree accuracy in the prediction result through the ANN method The results demonstrate that this method is widely available for the fields of dam safety monitoring and controlling