因子相关性对大坝监测模型精度的影响探究  

Analysis of Effects of Factor Correlativity on Accuracy of Dam Monitoring Models

作　　者：许后磊[1,2] 冯茂静 杨阳[1,2] 娄一青[1,2] 

机构地区：[1]河海大学水利水电工程学院,江苏南京210098 [2]河海大学水资源高效利用与工程安全国家工程研究中心,江苏南京210098 [3]温州市龙湾区水利局,浙江温州325000

出　　处：《水电能源科学》2009年第5期77-80,共4页Water Resources and Power

基　　金：国家自然科学基金资助重点项目(50809025;50539010);国家自然科学基金资助项目(50879024)

摘　　要：介绍了逐步回归、岭回归、偏最小二乘回归、RBF神经网络、主成分RBF组合模型的基本思路与特点。以陈村大坝变形计算为例,分别建立了各种回归模型,比较了各种模型的优缺点,指出线性统计模型中偏最小二乘回归法的拟合精度及解释能力优于逐步回归、岭回归法;RBF神经网络、主成分RBF组合模型优于线性统计模型,主成分RBF组合模型最优,拟合及预测精度最好。Article describes characteristics and the basic ideas of the stepwise regression, ridge regression, partial least- squares regression,RBF neural networks, principal components RBF combined model. To Chencun dam deformation, for example,a variety of regression models were established to compare the advantages and disadvantages of various models and pointed out that in a linear statistical model, partial least squares regression fitting precision and explanatory are power than in stepwise regression, ridge regression. RBF neural network, principal component RBF combined model is superior to linear statistical model. Principal component model for the optimal combination of RBF has the best fitting and forecasting accuracy.

关 键 词：因子相关性 统计回归模型 主成分分析 RBF神经网络模型 

分 类 号：TV698.1[水利工程—水利水电工程]