A NN-GM (1,1) MODEL-BASED ANALYSIS OF NETWORK TRAFFIC FORECASTING

摘要：

For the method of neural network can modify and better the forecasting effects of grey prediction model, a combined NN-GM (1,1) forecasting model is proposed. After the time sequence of the network traffic was analyzed, the equidistant metabolic GM (1,1) forecasting model was constructed, and the residual error of the model was corrected by neural network based on Error Back Propagation (BP) algorithm. The forecasting results of the experiments and the simulation show that the combined model is more effective than the single common grey model. The application of the proposed combined model to network traffic forecasting may offer scientific rationale for LAN virus detection, illegal invasion prevention and Internet routing decision.

关键词： Network Traffic metabolic-GM (1,1) Neural Network Forecasting Model

作者: Yan Bai Ke Ma Qingchang Ren

作者单位: Institute of Intelligent Buildings, Xi’an University of Architecture and Technology, Xi’an, China Sc School of foreign languages, Shaanxi Normal University, Xi’an, China Institute of Intelligent Buildings, Xi’an University of Architecture and Technology, Xi’an, China

会议类型: 国际会议

会议名称: 2010 3rd IEEE International Conference on Broadband Network & Multimedia Technology(2010年第三届IEEE宽带网络与多媒体国际会议 IC-BNMT 2010)

会议地点: 北京

会议语种:英文

页码: 191-194

在线出版日期: 2010-10-26（万方平台首次上网日期，不代表论文的发表时间）