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Neural Network Based Approaches for Network Traffic Prediction

  • Flávio Henrique Teles VieiraEmail author
  • Victor Hugo Teles Costa
  • Bruno Henrique Pereira Gonçalves
Part of the Studies in Computational Intelligence book series

Abstract

In this chapter, we review some learning strategies for neural networks such as MLP (Multilayer Perceptron), RBF (Radial Basis Function) and Recurrent Networks applied to computer network traffic prediction. That is, the considered neural networks and training algorithms are used to predict the traffic volume of a computer network. Some methods of improving the prediction performance of neural networks are also considered such as application of Wavelet Transform. We discuss about using the Wavelet Transform in supervised training of neural networks by decomposing the traffic process into approximation and detail processes. We present some results involving the application of the Orthogonal Least Squares (OLS) algorithm in RBF networks for traffic prediction. Regarding the Recurrent neural networks, we verify their traffic prediction performance when trained with the Extended Kalman Filter (EKF) and the RTRL (Real Time Recurrent Learning). Real network traffic traces are used in the simulations in order to verify the prediction performance of the neural network algorithms.

Keywords

Neural network Traffic prediction Recurrent Network RBF neural network Wavelets 

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Flávio Henrique Teles Vieira
    • 1
    Email author
  • Victor Hugo Teles Costa
    • 2
  • Bruno Henrique Pereira Gonçalves
    • 2
  1. 1.School of Electrical and Computer Engineering (EEEC)Federal University of Goiás (UFG)GoiâniaBrazil
  2. 2.Federal University of GoiásGoiâniaBrazil

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