【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。更多还原

【Abstract】 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 adaptability compared with the traditional linear model and BP neural network.更多还原