Towards traffic matrix prediction with LSTM recurrent neural networks-Reference-Cited by-同舟云学术

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Towards traffic matrix prediction with LSTM recurrent neural networks

Published:2018-05 Issue:9 Volume:54 Page:566-568
ISSN:0013-5194
Container-title:Electronics Letters
language:en
Short-container-title:Electron. lett.

Author:

Zhao Jianlong¹,Qu Hua¹,Zhao Jihong²,Jiang Dingchao³

Affiliation:

1. School of Software Engineering, Xi'an Jiaotong UniversityXi'an710049People's Republic of China

2. School of Telecommunication and Information Engineering, Xi'an University of Posts and TelecommunicationsXi'an710061People's Republic of China

3. School of Electronic and Information Engineering, Xi'an Jiantong UniversityXi'an710049People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/el.2018.0336

Reference8 articles.

1. Controlled synthesis of traffic matrices;Tune P.;Trans. Netw.,2017

2. Traffic matrices

3. Fast accurate computation of large-scale IP traffic matrices from link loads

4. Traffic matrix estimation: a neural network approach with extended input and expectation maximization iteration;Zhou H.;J. Netw. Comput. Appl.,2016

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