文档介绍：应用奇葩
Example of Application
基于径向基函数神经网络的网络流量识别模型
刘晓
(暨南大学信息科学与技术学院,广东广州 510000)
摘要: 提出了一种基于径向基函数神经网络的网络流量识别方法。根据实际网络中的流量数
据,建立了一个基于 RBF 神经网络的流量识别模型。先介绍了 RBF 神经网络的结构设计及学习算法,
针对 RBF 神经网络在隐节点过多的情况下算法过于复杂的缺点, 采用了优化的算法计算隐含层节
点。仿真实验证明,该模型具有较好的准确率、低复杂度、高识别效果和良好的自适应性。
关键词: RBF 神经网络;流量识别;流量分类
中图分类号: TP183 文献标识码: A 文章编号: 1674-7720(2012)02-0077-03
work traffic based on radial basis function work
Liu Xiao
(Computer Science Department, Jinan University, Guangzhou 510000 ,China )
Abstract : This paper presents a method work traffic identification based on RBF (Radial Basis Function) work.
With a large amount of real traffic data collected from the work, a work traffic model based on radial basis
function work theory was constructed to identify work traffic. Firstly present the structure design and leaning algo-
rithm of RBF work and then in order to reduce the plexity of the RBF when too many hide layer units ,pre-
sent an optimize algorithm to calculate the numbers of hide layer units. Finally prove this identification method in the application of
network traffic has the characteristics of high accuracy, plexity and high recognition efficiency, and the practical feasibility
in real-time traffic identification.
Key words : RBF work ;traffic identification ;traffic classification
随着互联网业务量的急剧增长网络性能和服务质提出的一种神经网络模型是具有单隐层的
, DARKEN C ,
量方面的问题日益突出在网络资源有限的情况下建前