摘要
以水口水电站坝体为例,基于Matlab中BP神经网络构建了效应量与环境量的网络模型,结合网络的权值和偏置值的综合影响,定量分析和研究了决定大坝效应量的水位、温度和时效等因素的重要性,并与传统的统计结果进行对比。结果表明,该方法可确定水位、温度等分量占效应量的比例,具有实用性和有效性。
Taking dam body of Shuikou Hydropower Station for an exmple,a neural network model of effect variable and effect factors is established on the application of BP neural network in Matlab.The proportions of water level,temperature and time effect factors affecting the effect variable are analyzed by considering integration of network weights and bias.Compared with the traditional statistical result,it shows that the proposed method is practical and effective,which can determine the effect variable proportion of water level and temperature.
出处
《水电能源科学》
北大核心
2011年第4期70-72,101,共4页
Water Resources and Power
关键词
大坝
BP神经网络
影响量
效应量
比例
dam
BP neural network
influence factors
effect variable
proportion
