Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering

Design method of campus network structure robustness

Authors
Lin Zhu
Corresponding Author
Lin Zhu
Available Online April 2015.
DOI
https://doi.org/10.2991/amcce-15.2015.271How to use a DOI?
Keywords
BP neural network; variable weight; robustness; network
Abstract
the traditional design of campus network adopts BP neural network to build the model for evaluating network information of network model, since the information exchange reflected in campus network is between individual and individual, randomness and model property factors of network information are prominent, BP neural network weights into curing condition, causes the information trust degree evaluation of large error. This paper presents an optimized robustness node design model, to construct information trust evaluation model. Setting up the trust degree respond weighting variable adaptive function periodically by adjusting network topologic weight vector, effectively reduce the operational cost of the iterative algorithm, and construct campus robust network. Simulation results show that, the new network information robustness evaluation model can reduce error rate of the website information evaluation greatly, the system has strong stability and robustness, can effectively promote the sound operation and development of network.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Lin Zhu
PY  - 2015/04
DA  - 2015/04
TI  - Design method of campus network structure robustness
BT  - Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.271
DO  - https://doi.org/10.2991/amcce-15.2015.271
ID  - Zhu2015/04
ER  -