Table of Contents Author Guidelines Submit a Manuscript
Discrete Dynamics in Nature and Society
Volume 2016 (2016), Article ID 4135056, 11 pages
http://dx.doi.org/10.1155/2016/4135056
Research Article

A Network Traffic Prediction Model Based on Quantum-Behaved Particle Swarm Optimization Algorithm and Fuzzy Wavelet Neural Network

School of Mathematics and Statistics, Chuxiong Normal University, Chuxiong, Yunnan 675000, China

Received 2 November 2015; Revised 14 January 2016; Accepted 3 February 2016

Academic Editor: Ahmed Kattan

Copyright © 2016 Kun Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO) was introduced. Then, the structure and operation algorithms of WFNN are presented. The parameters of fuzzy wavelet neural network were optimized by QPSO algorithm. Finally, the QPSO-FWNN could be used in prediction of network traffic simulation successfully and evaluate the performance of different prediction models such as BP neural network, RBF neural network, fuzzy neural network, and FWNN-GA neural network. Simulation results show that QPSO-FWNN has a better precision and stability in calculation. At the same time, the QPSO-FWNN also has better generalization ability, and it has a broad prospect on application.