Research on predicting network traffic using neural networks

Publication Type

Journal Article

Publication Date

10-2006

Abstract

This paper used Back-propagation (BP) algorithms and Davidon least squares-based learning algorithm to train the neural network (NN) to predict the nonlinear self-similar network traffic respectively. The feasibility and advantage of these two algorithms were discussed by analyzing the Mean learning errors, training errors and the convergent speed of these two training algorithms. The simulation demonstrated that the NN trained by both of these two training algorithms can well predict this traffic. Compared with BP algorithms, the Davidon least squares-based learning algorithm can converge quickly and has the almost same prediction accuracy. It supplied a feasible method to predict the complex self-similar network traffic.

Keywords

Back-propagation (BP) algorithms, Davidon least squares-based learning algorithm, Network traffic predicting, Neural network (NN)

Discipline

Numerical Analysis and Scientific Computing | OS and Networks

Research Areas

Intelligent Systems and Optimization

Publication

Guangdianzi Jiguang / Journal of Optoelectronics Laser

Volume

17

Issue

10

First Page

1255

Last Page

1258

ISSN

1005-0086

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