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General Information
    • ISSN: 2010-3751
    • Frequency: Bimonthly (2012-2016); Quarterly (Since 2017)
    • DOI: 10.18178/IJFCC
    • Editor-in-Chief: Prof. Mohamed Othman
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: Google Scholar, Engineering & Technology Digital Library, and Crossref, DOAJ, Electronic Journals LibraryEI (INSPEC, IET).
    • E-mail:  ijfcc@ejournal.net 
Editor-in-chief
Prof. Mohamed Othman
Department of Communication Technology and Network Universiti Putra Malaysia, Malaysia
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.
IJFCC 2013 Vol.2(2): 101-105 ISSN: 2010-3751
DOI: 10.7763/IJFCC.2013.V2.130

Network Traffic Prediction Based on the Wavelet Analysis and Hopfield Neural Network

Sun Guang

Abstract—Build a mathematical model is the key problem of network traffic prediction. Traditional single network flow model of is not simulate the complex characteristics of network traffic. Therefore, a network traffic prediction hybrid model based on αTrous wavelet analysis and Hopfield neural network is proposed in this paper, which can be used to predict the network traffic flow. First, network traffic is normalized and adopt αTrous wavelet transform; And then reconstruct the wavelet single, and predict through sending low frequency components into AR model and sending the high frequency component into Hopfield neural network model; Last, The predictive value are obtained by composing the components. Simulation results show that the model improves the prediction accuracy, and has the good adaptability to the network .

Index Terms—Network traffic, neural network, wavelet, Hopfield.

Sun Guang is with the College of Humanities and Information, Changchun University of Technology (e-mail: 9336631@qq.com).

[PDF]

Cite: Sun Guang, "Network Traffic Prediction Based on the Wavelet Analysis and Hopfield Neural Network," International Journal of Future Computer and Communication vol. 2, no. 2 pp. 101-105, 2013.

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