[1]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(04):454.[doi:10.14177/j.cnki.32-1397n.2017.41.04.009]
 Liu Yun,Jiao Yan,Wang Huadong.Chaotic prediction of network traffic based onimproved extreme learning machine[J].Journal of Nanjing University of Science and Technology,2017,41(04):454.[doi:10.14177/j.cnki.32-1397n.2017.41.04.009]
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改进极限学习机的网络流量混沌预测()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
41卷
期数:
2017年04期
页码:
454
栏目:
出版日期:
2017-08-31

文章信息/Info

Title:
Chaotic prediction of network traffic based onimproved extreme learning machine
文章编号:
1005-9830(2017)04-0454-06
作者:
刘 蕴1焦 妍2王华东3
1.周口职业技术学院 信息工程学院,河南 周口 466000; 2.河南应用技术职业学院 信息工程学院,河南 开封 475000; 3.周口师范学院 计算机科学与技术学院,河南 周口 466001
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
关键词:
网络流量 相空间重构 极限学习机 混沌变化特性
Keywords:
network traffic phase space reconstruction extreme learning machine chaos variation characteristics
分类号:
TP393
DOI:
10.14177/j.cnki.32-1397n.2017.41.04.009
摘要:
为了获得更加精确的网络流量预测,降低网络拥塞的频率,提出了改进极限学习机的网络流量预测模型。针对网络流量混沌性分别确定原始网络流量的延迟时间和嵌入维数,采用极限学习机对网络流量的变化特点进行拟合,改进标准学习机,改善学习速度和预测性能,最后通过网络流量数据的预测实验验证其可行性。验证结果表明:与其它网络流量预测模型相比,改进极限学习的网络流量预测结果更加可靠,对网络流量将来变化趋势可以更加准确描述,提高了网络流量预测精度。
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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备注/Memo

备注/Memo:
收稿日期:2017-01-09 修回日期:2017-03-16基金项目:国家自然科学基金(U1504613); 河南省高校科技创新团队计划(17IRTSTHN009)
作者简介:刘蕴(1973-),女,副教授,主要研究方向:计算机网络、物联网工程等,E-mail:liuyy858@tom.com; 通讯作者:王华东(1977-),男,硕士,副教授,主要研究方向:计算机网络与通信,E-mail:46935563@qq.com。
引文格式: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,2017,41(4):454-459.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2017-08-31