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偏最小二乘回归与灰色模型耦合预测城市用水量
引用本文:李林,付强. 偏最小二乘回归与灰色模型耦合预测城市用水量[J]. 长江科学院院报, 2008, 25(4): 20-23
作者姓名:李林  付强
作者单位:塔里木大学农业工程学院,新疆,阿拉尔,843300;东北农业大学水利与建筑学院,哈尔滨,150030
基金项目:塔里木大学校长基金资助项目
摘    要: 影响城市用水量的各个因素,存在多重相关性,采用传统最小二乘回归法建模,其估计参数存在较大误差,预测精度降低。运用偏最小二乘回归法建立城市用水量的预测模型可以克服变量间的多重相关性影响,并可以很好地解释因变量;采用GM(1,1)建立的城市用水量预测模型,能够克服参数的非线性干扰,进行中长期预测。结果和实际符合,将两者进行耦合,充分利用了两种模型的优点,预测结果更为合理可靠。

关 键 词:偏最小二乘回归  灰色预测模型  耦合  城市用水量  预测

Coupling Partial Least-Squares Regression with Grey Model(1,1) to Forecaste Urban Water Consumption
LI Lin,FU Qiang. Coupling Partial Least-Squares Regression with Grey Model(1,1) to Forecaste Urban Water Consumption[J]. Journal of Yangtze River Scientific Research Institute, 2008, 25(4): 20-23
Authors:LI Lin  FU Qiang
Affiliation:LI Lin1,FU Qiang2(1.School of Agricultural Engineering,Tarim University,Alar 843300,China,2.College ofWater Conservancy & Civil Engineering,Northeast Agricultural University,Harbin 150030,China)
Abstract:There is multi-correlation among the factors which affect the city water consumption.The result obtained by the traditional least sequare method has a greater error than the true value.The partial least-square regression(PLS) is applied to set up the urban water use model based on the main component analysis and typical correlation analysis,it can solve the problem of interactive correlation among the independent variables and explain the dependent variables very well.The GM(1,1) model which is adopted to b...
Keywords:partial least-square regression(PLS)  grey model(1  1)  coupling  urban water consumption  forecasting  
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