Network Traffic Prediction Based on Feed-forward Neural Network with PLS Pruning Algorithm

DOI

10.2991/ameii-16.2016.192How to use a DOI?

Keywords

Network Traffic Prediction, FNN, PLS, Pruning Algorithm

Abstract

To improve the prediction accuracy and reduce the computational complexity of network traffic prediction based on feed-forward neural network (FNN), the partial least squares (PLS) pruning algorithm was proposed to optimize the network topology structure. The data of network traffic has the characteristics of burst, nonlinear and time variation, which results in the traditional neural network has the disadvantages of slow convergence rate and easy to fall into local minimum for network traffic prediction. The performance of FNN is closely related to the number of nodes in the hidden layer, which affects the computational complexity, convergence rate and convergence accuracy. The proposed method uses PLS pruning algorithm to simply the network topology structure, which can obtain the ideal number of hidden layer nodes of the FNN, and then the prediction accuracy of network traffic is improved. The computer simulation results show that the proposed method has faster convergence rate and higher convergence accuracy compared with traditional FNN.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhenxing Li
AU  - Qinghai Meng
PY  - 2016/04
DA  - 2016/04
TI  - Network Traffic Prediction Based on Feed-forward Neural Network with PLS Pruning Algorithm
BT  - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
PB  - Atlantis Press
SP  - 1006
EP  - 1010
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-16.2016.192
DO  - 10.2991/ameii-16.2016.192
ID  - Li2016/04
ER  -