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Predicting nonlinear network traffic using fuzzy neural network

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4 Author(s)
Zhaoxia Wang ; Dept. of Autom., Nankai Univ., Tianjin, China ; Tingzhu Hao ; Zengqiang Chen ; Zhuzhi Yuan

Network traffic is a complex and nonlinear process 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 inertial 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.

Published in:

Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference on  (Volume:3 )

Date of Conference:

15-18 Dec. 2003