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《Science of Surveying and Mapping》 2014-11
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Application of penalized least squares method in gray forecasting model

PAN Shen-yun;School of Geodesy and Geomatics,Anhui University of Science and Technology;  
In the paper,grey forecasting model and semi-parametric model were discussed,as to the model errors of classical least squares in calculating the recognized vector of GM(1,1)Model,penalized least squares method that regards the model error as non-parametric information was used to process data.According to the characteristics of mining deformation monitoring,the selection of regularizer Rand smoothing parameterαwere described briefly,and the corrected model was tested and analyzed with observed data of the surface displacement observation station of Zhuji Mine in Huainan City.The result showed that the method could weaken the influence of systematic errors of the model effectively and improve the accuracy of the forecasting model.
【CateGory Index】: N941.5
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