基于Plane-Gaussian神经网络的网络流状态监测  

Network traffic classification based on Plane-Gaussian artificial neural network

作　　者：杨绪兵[1] 冯哲[1] 顾一凡 薛晖[2] 

机构地区：[1]南京林业大学信息科学技术学院,南京210037 [2]东南大学计算机科学与工程学院,南京210096

出　　处：《计算机应用》2017年第3期782-785,816,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61375057);江苏高校品牌专业建设工程资助项目~~

摘　　要：针对复杂网络环境下网络流监测(分类)问题,为实现多个类别直接分类以及提高学习方法的训练速度,提出了一种随机的人工神经网络学习方法。该方法借鉴平面高斯(PG)神经网络模型,引入随机投影思想,通过计算矩阵伪逆的方法解析获得网络连接矩阵,理论上可证明该网络具有全局逼近能力。在人工数据和标准网络流监测数据上进行了实验仿真,与同样采用随机方法的极限学习机(ELM)和PG网络相比,分析与实验结果表明:1)由于继承了PG网络的几何特性,对平面型分布数据更为有效;2)采用了随机方法,训练速度与ELM相当,但比PG网络快得多;3)三种方法中,该方法更有利于解决网络流监测问题。Aiming at the problems of network flow monitoring （classification） in complex network environment, a stochastic artificial neural network learning method was proposed to realize the direct classification of multiple classes and improve the training speed of learning methods. Using Plane-Gaussian （PG） artificial neural network model, the idea of stochastic projection was introduced, and the network connection matrix was obtained by calculating the pseudo-inverse analysis. Theoretically, it can be proved that the network has global approximation ability. The artificial simulation was carried out on artificial data and standard network flow monitoring data. Compared with the Extreme Learning Machine （ELM） and PG network using the random method, the analysis and experimental results show that： 1） the proposed method inherits the geometric characteristics of the PG network and is more effective for the planar distributed data; 2） it has comparable training speed to ELM, but significantly faster than PG network; 3） among the three methods, the proposed method is more suitable for solving the problem of network flow monitoring.

关 键 词：Plane-Gaussian人工神经网络 极限学习机 随机投影 全局逼近 分类精度 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]