Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

Risk assessment method of coal and gas outburst based on T-S fuzzy neural network

Authors
Yu Zhang, Wen Yang, Xinzhi Yu, Mingxuan Zhou, Chengpu Liu
Corresponding Author
Yu Zhang
Available Online March 2017.
DOI
https://doi.org/10.2991/ifmca-16.2017.31How to use a DOI?
Keywords
T-S fuzzy neural network; Coal and gas outburst; Risk assessment
Abstract
Based on coal and gas outburst factors, combined with single gas risk index and comprehensive evaluation method to select evaluation index, in order to evaluate coal and gas outburst, evaluation of coal and gas outburst is established t-s fuzzy neural network model. Combined fuzzy logic and neural network, the model is realized in matlab software fuzzy neural network building, training, so as to realize the purpose of network evaluation. A Case Study of Pingdingshan No.13 Mine to evaluate the danger of coal and gas outburst, the final forecasting result is the same as actual results and different methods to predict the results of other researchers, suggests that feasibility and rationality of T-S fuzzy neural network model, offers a new way for gas outburst risk assessment..
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
Series
Advances in Engineering Research
Publication Date
March 2017
ISBN
978-94-6252-307-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/ifmca-16.2017.31How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yu Zhang
AU  - Wen Yang
AU  - Xinzhi Yu
AU  - Mingxuan Zhou
AU  - Chengpu Liu
PY  - 2017/03
DA  - 2017/03
TI  - Risk assessment method of coal and gas outburst based on T-S fuzzy neural network
BT  - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
PB  - Atlantis Press
SP  - 193
EP  - 197
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifmca-16.2017.31
DO  - https://doi.org/10.2991/ifmca-16.2017.31
ID  - Zhang2017/03
ER  -