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《Urban Geotechnical Investigation & Surveying》 2008-01
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Research on a Residual Amendment Method of GM(1,1)Predictive Model Based on Neural Network

Guang Hui,Li GuoFen(Nanjing Forestry University,Nanjing 210037,China)  
With the shortcoming of GM(1,1)model predictive accuracy is low in a longer period,a new residual amendment method of GM(1,1)prediction model is put forward in this paper and the method based on neural network.Compared with the traditional residual GM(1,1)model correction method and verified by an engineering example,the method proposed in this paper can improve forecast accuracy effectively and extend forecast period and it has better application in engineering.
【CateGory Index】: TU196.1
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【Citations】
Chinese Journal Full-text Database 3 Hits
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【Co-citations】
Chinese Journal Full-text Database 10 Hits
1 ZHANG Gui-sheng1,LIU Xin-rong2,HU Yuan-xin2(1.Zhejiang Huang-Qu-Nan Expressway Co.Ltd.,Quzhou,Zhejiang 324000,China;2.College of Civil Engineering,Chongqing University,Chongqing 400030,China);Practice and Intellection for Projectized Management of Information Monitoring of Tunnel Construction[J];Chinese Journal of Underground Space and Engineering;2009-01
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3 Sun Shiguo~(1)) Xi Genxi~(1)) Wei Hanbo~(2)) Tan Liang~(1))(1)College of Architecture Engineering,North China Univ.of Tech.,100041,Beijing,China 2)College of Civil and Resource,University of Science & Technology Beijing,100083,Beijing,China);Study on Calculation of Deformation of Underground Pipework Caused by Deep Pit Excavation[J];Journal of North China University of Technology Beijing China;2006-03
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7 FANG Yi1,2,HUA Xiang-hong1,2,LI Hai-ying1,2,HAN Hong-chao1,2(1.Hazard Monitoring and Prevention Research Center,Wuhan University,Wuhan 430079,China;2.School of Geodesy and Geomatics,Wuhan University,Wuhan 430079,China);The application of grey neural network model to deformation forecasting of buildings[J];Engineering of Surveying and Mapping;2008-02
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