|Table of Contents|

Chaotic prediction of network traffic based onimproved extreme learning machine(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2017年04期
Page:
454-
Research Field:
Publishing date:

Info

Title:
Chaotic prediction of network traffic based onimproved extreme learning machine
Author(s):
Liu Yun1Jiao Yan2Wang Huadong3
1.School of Information Engineering,Zhoukou Vocational and Technical College,Zhoukou 466000,China; 2.School of Information Engineering,Henan Vocational College of Applied Technology,Kaifeng 475000,China; 3.School of Computer Science and Technology,Zhouk
Keywords:
network traffic phase space reconstruction extreme learning machine chaos variation characteristics
PACS:
TP393
DOI:
10.14177/j.cnki.32-1397n.2017.41.04.009
Abstract:
In order to obtain more accurate prediction of network traffic and reduce the congestion frequency of network,a novel network traffic prediction model based on improved extreme learning machine is proposed in this paper.Firstly,the delay time and embedding dimension are determined according to the chaos of network traffic,and secondly,extreme learning machine is used to simulate the change rule of network traffic which standard learning machine is improved to improve the learning speed and performance,finally,the feasibility of is verified by the network traffic data.The results show,the network traffic prediction results of the proposed model are more reliable Compared with other network traffic prediction models,can describe the change trend of network traffic and improves the prediction accuracy of network traffic.

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Last Update: 2017-08-31