基于RBF神经网络的网络流量建模及预测  

Network traffic modeling and prediction based on RBF neural network

作　　者：王俊松[1] 高志伟[1] 

机构地区：[1]天津大学自动化系,天津300270

出　　处：《计算机工程与应用》2008年第13期6-7,11,共3页Computer Engineering and Applications

基　　金：国家自然科学基金(the National Natural Science Foundation of China under Grant No.60472078)

摘　　要：随着计算机网络的迅速发展,目前的网络规模极为庞大和复杂,网络流量预测对于网络管理具有至关重要的意义。根据实际网络中测量的大量网络流量数据,建立了一个基于RBF神经网络的流量模型,给出了RBF神经网络的结构设计及基于正交最小二乘的学习算法,并基于该流量模型对网络流量进行预测。仿真结果表明,该模型具有较高的预测效果,相对于传统线性模型及BP神经网络模型具有更高的预测精度和良好的自适应性。With the rapid development,the network now has a large size and high complexity,and consequently the network management is becoming increasingly difficult,so traffic prediction play more and more important role in network management.With a large amount of real traffic data collected from the actual network,a nonlinear network traffic model based on Radial Basis Function（RBF） neural network theory was constructed to predict the network traffic.The structure design and leaning algorithm of RBF neural network is presented.The simulation results on real network traffic show that the proposed RBF based prediction scheme is efficient,and has better precision and adptability compared with the traditional linear model and BP neural network.

关 键 词：RBF神经网络 网络流量 建模 预测 

分 类 号：TP393.07[自动化与计算机技术—计算机应用技术]