Predicting nonlinear network traffic using fuzzy neural network

• Main Authors: WANG, Zhaoxia, HAO, Tingzhu, CHEN, Zengqiang, YUAN, Zhuzhi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2003
• Subjects: Back-propagation algorithms; Fuzzy neural network; Inertial terms; Traffic prediction; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/5489; https://ink.library.smu.edu.sg/context/sis_research/article/6492/viewcontent/Predicting_nonlinear_network_traffic_fuzzyNeuralNetwork_ICICS03_av.pdf
• Institution: Singapore Management University

Network traffic is a complex and nonlinear process, which is significantly affected by immeasurable parameters and variables. This paper addresses the use of the five-layer fuzzy neural network (FNN) for predicting the nonlinear network traffic. The structure of this system is introduced in detail. Through training the FNN using back-propagation algorithm with inertia] terms the traffic series can be well predicted by this FNN system. We analyze the performance of the FNN in terms of prediction ability as compared with solely neural network. The simulation demonstrates that the proposed FNN is superior to the solely neural network systems. In addition, FNN with different fuzzy reasoning approaches is discussed.