首页>成果

  • 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

[会议论文]

Research on network traffic prediction based on long short-term memory neural network

分享
编辑 删除 报错

作者:

Lu, Haipeng (Lu, Haipeng.) | Yang, Fan (Yang, Fan.)

收录:

EI Scopus

摘要:

Because of the burstiness and uncertainty of network, the prediction for short-term network traffic is a difficult problem. This paper proposes a real-time network traffic prediction model based on Long Short-Term Memory (LSTM) neural network. The loss function of LSTM network is modified to enhance the robustness of the prediction model. Different from the traditional LSTM model, the proposed model is continually updated with the arrival of new traffic. The experimental results show that the proposed model performs better on prediction accuracy than other models constructed with Support Vector Regression and Back Propagation neural network. © 2018 IEEE.

关键词:

Support vector regression Brain Forecasting Long short-term memory Backpropagation Computer networks Traffic control Predictive analytics

作者机构:

  • [ 1 ] [Lu, Haipeng]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Yang, Fan]State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

通讯作者信息:

查看成果更多字段

相关文章:

来源 :

年份: 2018

页码: 1109-1113

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 18

近30日浏览量: 0

归属院系:

在线人数/总访问数:265/4508312
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司