Study on detection method for mixed network structure robustness in 4G network

DOI

10.2991/amcce-15.2015.323How to use a DOI?

Keywords

BP neural network; robustness; 4G network;

Abstract

detection method for mixed network structure robustness in 4G network is researched. BP neural network algorithm is utilized to detect mixed network structure robustness in 4G network, because the 4G network reflects the exchange of information between individual and individual, randomness factors of network information is prominent, weights of BP neural network transmit into solid condition, leading to large detection error for robustness. Therefore, this paper proposes a fuzzy correlation theory to construct the detection model for mixed network structure robustness in 4G network. On the basis of given new robustness measure for mixed network structure in 4G network, according to small domain communication efficiency and the size of connected graph, the two attribute are utilized to calculate the robustness of single node of mixed network structure in 4G network, the quantized robust value is divided into two independent feature subspace and non feature subspace, in the corresponding space, the correlation robustness of region nodes is calculated, so as to complete robustness detection of mixed network structure in 4G network. Simulation results show that, the new 4G network mixed network structure robustness detection model can reduce evaluation error of mixed network structure in 4G network greatly, the system has better robustness, can effectively promote the operation and development of 4G network.

Copyright

© 2015, 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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TY  - CONF
AU  - Hui-min Chen
PY  - 2015/04
DA  - 2015/04
TI  - Study on detection method for mixed network structure robustness in 4G network
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-15.2015.323
DO  - 10.2991/amcce-15.2015.323
ID  - Chen2015/04
ER  -