Early Warning System Design of Safe Production of Coal Mine Based on Improved BP Neural network

DOI

10.2991/icmmct-16.2016.405How to use a DOI?

Keywords

BP neural network, improved algorithm, safe production of coal mine, early warning

Abstract

For staff, environment, equipment and management the four aspects, the early warning index system of coal mine safety production adopts the additional momentum method and adaptive vector, particle swarm optimization (PSO) algorithm and the method of variable weight and asynchronous learning factor and other measures. It is to optimize the BP neural network model and apply the model to the early warning system of coal mine safety, and at the same time, it also compared with other neural network model. The simulation result shows that the recognition accuracy of improved BP neural network model is higher than PSO-BP model, the model not only can effectively reduce the possibility of a network get into a local minimum point, but also have the characteristics of fast convergence speed, high accuracy and etc., and it can provide a scientific basis to the coal mine production safety early warning management.

Copyright

© 2016, 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  - Hong-Li Qiu
AU  - Pei-shuo Sun
PY  - 2016/03
DA  - 2016/03
TI  - Early Warning System Design of Safe Production of Coal Mine Based on Improved BP Neural network
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 2030
EP  - 2033
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.405
DO  - 10.2991/icmmct-16.2016.405
ID  - Qiu2016/03
ER  -