基于样条权函数神经网络P2P流量识别方法  

P2P Traffic Identification Based on Spline Weight Function Neural Network

作　　者：侯善江 张代远[1,2,3] 

机构地区：[1]南京邮电大学计算机学院,江苏南京210003 [2]江苏省无线传感网高技术研究重点实验室,江苏南京210003 [3]南京邮电大学计算机技术研究所,江苏南京210003

出　　处：《计算机技术与发展》2014年第7期21-24,共4页Computer Technology and Development

基　　金：江苏高校优势学科建设工程资助项目(yx002001)

摘　　要：样条权函数神经网络是一种新兴的神经网络,克服了很多传统神经网络(如BP、RBF)的缺点:比如局部极小、收敛速度慢等。它具有拓扑结构简单,精确记忆训练过的样本,反映样本的信息特征,求得全局最小值等优点。基于这些优点,文中提出了一种基于样条权函数神经网络P2P流量识别方法。通过提取P2P流量特征,运用样条权函数神经网络结构对P2P流识别。Matlab仿真和模拟实验结果表明了这种方案的可行性,与传统神经网络相比,样条权函数神经网络在时间效率上具有明显优势。Spline weight function neural network is a new kind of neural network. It overcomes many defects of traditional neural networks （ like BP,RBF） ,such as local minima,slow convergence,at the same time has many advantages,such as simple structure,remembering trained samples,reflecting the characteristics of the sample information,finding global minima directly and so on. A method of P2P traffic identification based on spline weight function neural network is presented in this paper based on advantages of this neural network. The structure of spline weight function neural network can identify P2P traffic by extracting characteristics of P2P traffic training. Matlab sim-ulation and experimental results show the feasibility of the scheme. Compared with the traditional neural network,spline weight function neural network has obvious advantages in time efficiency.

关 键 词：样条权函数 神经网络 P2P 流量识别 插值 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]