基于补偿器的灰色神经网络流量预测模型  

Network traffic prediction based on compensated grey neural network model

作　　者：刘渊[1] 曹建华[2] 戴悦[2] 

机构地区：[1]南京理工大学计算机学院,南京210094 [2]江南大学信息工程学院,江苏无锡214122

出　　处：《计算机应用》2007年第9期2224-2226,共3页journal of Computer Applications

基　　金：国防基础研究基金资助项目(A1420061266)

摘　　要：为衡量网络运行负荷和运行状态,对网络进行合理规划,在对目前网络流量预测模型进行了研究的基础上,结合灰色模型和神经网络模型在反映数据的趋势性变化上的明显效果,以及神经网络补偿器,提出了基于补偿器的灰色神经网络流量预测模型,仿真结果验证了所提方法的有效性。To measure the workload and state of network operation, a predictable algorithm based on the grey model, neural network and component error was presented. The present network traffic model has been studied, and the prominent effect in reflecting the variable trend of data has been combined with the grey model and neural network. The simulation results show that the integrated model can improve the prediction precision obviously compared to the other algorithms.

关 键 词：网络流量 灰色模型 神经网络 神经网络补偿器 预测 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]