Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)

Technology Using Neural Network Optimization Path in Packet Switching Network

Authors
Shoubai Xiao
Corresponding Author
Shoubai Xiao
Available Online February 2017.
DOI
10.2991/emcm-16.2017.116How to use a DOI?
Keywords
Dynamic network; Neural Network; Genetic Algorithm; The Shortest Path
Abstract

How to find the optimal path between two nodes has been one of the most difficult problems in the internet. In this paper, a method of finding the optimal path based on neural network technology is proposed, which solves the problem of finding the optimal path by adjusting the weight of the neuron. The results show that the algorithm proposed in this paper is simple in calculation and fast in convergence speed, and it is suitable for the research on the optimal path of packet switching. Although the shortest path algorithm has been established, the researchers are still studying other better path selection methods.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)
Series
Advances in Computer Science Research
Publication Date
February 2017
ISBN
978-94-6252-297-8
ISSN
2352-538X
DOI
10.2991/emcm-16.2017.116How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Shoubai Xiao
PY  - 2017/02
DA  - 2017/02
TI  - Technology Using Neural Network Optimization Path in Packet Switching Network
BT  - Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/emcm-16.2017.116
DO  - 10.2991/emcm-16.2017.116
ID  - Xiao2017/02
ER  -