年均径流预测的遗传神经网络模型研究  

Study and Application on Genetic-Neural Network Prediction Model for the Annual Average Runoff of Reservoir

作　　者：吴海波[1] 赵晓慎[1] 王文川[1] 

机构地区：[1]华北水利水电学院,河南郑州450011

出　　处：《人民黄河》2012年第4期37-38,41,共3页Yellow River

基　　金：河南省教育厅自然科学研究计划项目(2010B570002);华北水利水电学院高层次人才科研启动项目(200821)

摘　　要：为了进一步提高BP网络模型对径流预测的精度,采用遗传算法优化了BP网络初始的权值和阈值。实例研究结果表明:该方法克服了传统BP网络极易陷入局部极小值点等缺点,提出的遗传神经网络预测模型能够提高预测精度。In order to improve the predication accuracy of back propagation network model for annual runoff of reservoir,back propagation network had been used to optimize the weights and threshold by genetic algorithm of prediction model.The study results show that,this method overcomes the shortcomings that traditional back propagation network traps into local minima easily,the genetic-neural network model has a higher accuracy than that of traditional BP network.

关 键 词：遗传算法 BP神经网络 径流预测 

分 类 号：P333[天文地球—水文科学]