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| SOFM neural network model for prediction of coal and gas outburst and its application |
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Received:December 06, 2017
Revised:December 14, 2017
Accepted:December 15, 2017
Published Online:March 29, 2018
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| DOI： |
| KeyWord:coal and gas outburst;predicting;Self-organization feature map;risk;neural network |
| Author | Institution |
| LIU Chen-yu |
Department of land and resources engineering，Kunming University of Science and Technology |
| CHEN Jun-zhi |
Kunming University of Science and Technology |
| XU Jia |
Kunming University of Science and Technology |
| LONG Gang |
Kunming University of Science and Technology |
| LI Chun-yi |
Kunming University of Science and Technology |
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| Abstract: |
| Coal and gas outburst is one of the most common dynamic disasters in coal mine production activities, and its hazard rating is the prerequisite and guarantee of coal mine safety production. Article comprehensively consider the stress of coal and gas outburst occurred, the physical and mechanical properties of gas and coal, geological damage, gas pressure, gas radiation initial velocity and the consistence coefficient of coal and mining depth as the danger of coal and gas outburst prediction index. Based on this, this article borrow sign a self-organizing feature map (SOFM) neural network, establish risk prediction of coal and gas outburst of SOFM neural network model of SOFM neural network model was applied to 26 domestic typical risk prediction of coal and gas outburst mine. The research shows that SOFM neural network model has good prediction effect, and its misjudgment rate is 92.31%. The model can provide a new way for the prediction of coal and gas outburst of small sample and multi-index. |
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