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

Kun Zhang,Zhao Hu,Jian-Bo Fang,Xiao-Ting Gan

doi:10.1155/2016/4135056

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.

Highlights

With the rapid development of computer network technology, network applications have infiltrated every corner of human society and play an important role in various industries and situations
The architecture of prediction model based on fuzzy wavelet neural network and genetic algorithm (FWNN-genetic algorithms (GA)) is the same with the quantum-behaved particle swarm optimization (QPSO)-FWNN method
Since the network traffic is affected by many factors, the data of network traffic have the volatility and self-similarity features and the network traffic prediction becomes a challenge task

Summary

Introduction

With the rapid development of computer network technology, network applications have infiltrated every corner of human society and play an important role in various industries and situations. Since the network topology structure is gradually complicated, the problem of network’s emergencies and congestion are more and more serious. Through monitoring and accuracy prediction of network traffic, it can prevent network congestion and can effectively improve the utilization rate of the network [1]. The network traffic data is a kind of time series data and the problem of network traffic prediction is to forecast future network traffic rate variations as precisely as possible based on the measured history. As the network traffic is affected by many factors, the network traffic time series show quite obvious multiscale, long-range dependence, and nonline characteristic. The methods mentioned above have the weakness of lowlevel efficiency [6]

Methods

Results

Conclusion