Prediction Strategy of Coal and Gas Outburst Based on Artificial Neural Network

被引:7
作者
Wang, Fuzhong [1 ]
Liu, Weizhe [1 ]
机构
[1] Henan Polytech Univ, Dept Elect Engn & Automat, Jiaozuo, Peoples R China
关键词
artificial neural network; coal and gas outburst prediction; gas content;
D O I
10.4304/jcp.8.1.240-247
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The article describes the research of coal and gas outburst prediction technology and the new problems they face in the modern mining. It also describes the superiority of neural network technology in dealing with complex geological conditions. It refers to the possibility and necessity of combination of the coal and gas outburst prediction and artificial neural networks, and other high-technology, There are examples show that they can be applied to predict the course of coal and gas outburst and gas content. Practice has proved the prediction model that coal and gas outburst forecasting techniques and artificial neural network have established not only considers the various factors and better handle various kinds of the non-linear relationships in geological conditions, but also having a forecast of high precision and reliable conclusions and provides a new way about the further development of coal and gas outburst prediction technology.
引用
收藏
页码:240 / 247
页数:8
相关论文
共 18 条
[1]  
Aizhu Wei, 2004, MINING SAFETY ENV PR, V33, P4
[2]  
Caifang Wu, 2003, EARTH SCI FRONTIERS, V10, P219
[3]   FPGA implementation of neural network based adaptive control of a flexible joint with hard nonlinearities [J].
Chaoui, Hicham ;
Sicard, Pierre ;
Lakhsasi, Ahmed .
2006 IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS, VOLS 1-7, 2006, :3118-+
[4]  
Guanmao Wu, 2008, COAL GEOLOGY EXPLORA, V36, P30
[5]  
Hongfei Xiao, 2004, PROGR SAFETY SCI TEC, P431
[6]  
Hua Fu, 2002, J COAL SCI ENG, V8, P80
[7]  
Jisheng Hao, 2004, J LIAONING TU, V30, P9
[8]  
Lei Jiang, 2009, MINE SAFETY PROTECTI, V37
[9]  
Linyan Fu, 2006, COAL MINE ELECT, V6, P41
[10]   FPGA implementation of a pulse density neural network with learning ability using simultaneous perturbation [J].
Maeda, Y ;
Tada, T .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2003, 14 (03) :688-695