Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2003

Abstract

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.

Keywords

Back-propagation algorithms, Fuzzy neural network, Inertial terms, Traffic prediction

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Publication

ICICS-PCM 2003: Proceedings of the 2003 Joint Conference of the Fourth International Conference on Information, Communications & Signal Processing and Fourth Pacific Rim Conference on Multimedia, December 15-18, Singapore

First Page

1697

Last Page

1701

ISBN

9780780381858

Identifier

10.1109/ICICS.2003.1292756

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/ICICS.2003.1292756

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