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《Computer Engineering》 2006-12
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Time Series Forecasting Model Based on Neural Network with Multi-rule & Real-time Training

XING Lining1,CHEN Yingwu1,LIU Hejun2(1.School of Information System and Management,National Univ.of Defense Technology,Changsha 410073;2.Army of 72556 Unit,PLA)  
Based on the limitation of existing neural network model,this paper puts forward the thought of this model: miniaturizes the forecasting network,trains the network in real-time way,adopts the average of abundant forecasting,adds some rules to assistant forecasting.Relative to the traditional neural network model,this model focuses on dynamic training and dynamic forecasting,increases three rules(rule of dealing with abnormity,rule of retraining,and rule of adopting the average) to assistant forecasting.It presents the computing flow of this model and explains the whole process of this model with an example.Numerical example suggests the correctness and feasibility of this model.The contradistinctive result of this model and other five models indicates the validity and superiority of this model.
【Fund】: 国家自然科学基金资助项目(70272002)
【CateGory Index】: TP183
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【References】
Chinese Journal Full-text Database 1 Hits
1 XIA Dong-liang,GONG Yan-feng (Nanjing University of Technology,Nanjing 210009,China);Prediction Model of Urban Building Energy Consumption Based on BP Neural Network with Multi-rules & Real-time Training[J];Building Science;2008-06
【Citations】
Chinese Journal Full-text Database 1 Hits
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【Co-citations】
Chinese Journal Full-text Database 1 Hits
1 By WU Fei1, TANG Liang-hong2, FENG Yu-Qiang1, HUENG Ti-yun1(1.Harbin Institute of Technology; 2.Harbin University of Science and Technology,Harbin 150001);A Model Selection Approach to Time Series Identification by Forecasting Model Decision Tree[J];Journal of Industrial Engineering and Engineering Management;2001-01
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