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: firstname.lastname@example.org).
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.