南京理工大学学报（自然科学版）2017，Vol.41Issue(4)：454-459,6.DOI:10.14177/j.cnki.32-1397n.2017.41.04.009
改进极限学习机的网络流量混沌预测
Chaotic prediction of network traffic based onimproved extreme learning machine
摘要
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.关键词
网络流量/相空间重构/极限学习机/混沌变化特性Key words
network traffic/phase space reconstruction/extreme learning machine/chaos variation characteristics分类
信息技术与安全科学引用本文复制引用
刘蕴,焦妍,王华东..改进极限学习机的网络流量混沌预测[J].南京理工大学学报（自然科学版）,2017,41(4):454-459,6.基金项目
国家自然科学基金(U1504613) （U1504613）
河南省高校科技创新团队计划(17IRTSTHN009) （17IRTSTHN009）
