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
Citation
1