改进 BP 神经网络集成模型在径流预测中的应用OACSTPCD
Application of integrated model of improved BP neural network in prediction of runoff
为提高径流预测预报的精度和泛化能力，建立了基于3种基本改进算法的BP神经网络集成预测模型。利用ADF单位根检验方法、自相关分析方法确定径流时间序列的平稳性和模型的输入向量。针对BP神经网络标准算法收敛速度慢、易陷入局部极值的缺陷，采用自适应动量梯度法、共轭梯度法和Levenberg-Marquardt 法分别改进BP神经网络标准算法，依次构建基于3种改进算法的BP神经网络模型对文山州南利河董湖水文站年径流进行预测，并构建GA-BP预测模型作为对比…查看全部>>
In order to improve the precision and generalization ability of runoff forecast , the paper put forward integrated forecast model of BP neural network based on 3 kinds of basic improved algorithm .The autocorrelation analysis method and ADF unit root test method are used to determine input vector and smooth model of runoff time series .According to the standard of BP neural network algorithm with slow convergence , easy to fall into local minimum defect resp…查看全部>>
杨洪
云南省水文水资源局文山分局，云南文山663000
建筑与水利
径流集成模型BP神经网络改进算法加权平均径流预测
runoffintegrated modelBP neural networkimproved algorithmweighted averagerunoff forecast
《水资源与水工程学报》 2014 (3)
213-219,7
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