摘要
大坝渗流观测资料分析中,各因子间常存在不同程度的相关性,这种相关性有时会对分析效果产生较大的影响,另外,通常的回归模型为线性模型,难以精确反映一般为非线性函数的因变量的变化规律。针对上述问题,本文将主成分分析和神经网络相结合,建立大坝渗流观测数据的主成分神经网络模型,经实例计算,该模型的预报精度较高。
Correlation between factors has an effect on the analysis of dam seepage observation data. Besides, the regression model is usually linear and it can hardly reflect the change of a dependent variable which is nonlinear function. In consideration of the above problems, and in combination with principal component analysis and BP neural network. A principal component analysis and BP neural network model is set up for analysis of dam seepage observation data in this paper. The result of an example shows that the prediction is more precise than that of the regression model.
出处
《大坝与安全》
2009年第5期29-31,35,共4页
Dam & Safety
关键词
大坝渗流
主成分分析
BP神经网络
预报
dam seepage
principal component analysis
BP neural network
prediction
