WPANFIS:combine fuzzy neural network with multiresolution for network traffic prediction

作     者：LI Rui CHEN Jian-ya LIU Yun-jie WANG Zhen-kai 

作者机构：School of Information and Telecommunication Engineering Beijing University of Posts and Telecommunications Beijing 100876 China China Unicorn Beijing 100140 China 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2010年第17卷第4期

页      面：88-93页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 081201[工学-计算机系统结构] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Basic Research Program of China (2007CB310701) Research Fund for University Doctor Subject (20070013013) Chinese Universities Scientific Fund (2009RC0124) 

主　　题：network traffic prediction WPT ANFIS 

摘      要：A novel methodology for prediction of network traffic, WPANFIS, which relies on wavelet packet transform （WPT） for multi-resolution analysis and adaptive neuro-fuzzy inference system （ANFIS） is proposed in this article. The widespread existence of self-similarity in network traffic has been demonstrated in earlier studies, which exhibits both long range dependence （LRD） and short range dependence （SRD）. Also, it has been shown that wavelet decomposition is an effective tool for LRD decorrelation. The new method uses WPT as extension of wavelet transform which can decoorrelate LRD and make more precisely partition in the high-frequency section of the original traffic. Then ANFIS which can extract useful information from the original traffic is implemented in this study for better prediction performance of each decomposed non-stationary wavelet coefficients. Simulation results show that the proposed WPANFIS can achieve high prediction accuracy in real network traffic environment.