Name
Abstract | ||
|---|---|---|
Back-propagation neural network model was developed to predict the coal and gas outburst. After trained, the artificial neural network model was used to predict the coal and gas outburst of several samples. Moreover, ANN model was also used to analyse the quantitative effects of influencing factors on the coal and gas outburst. The prediction performance of ANN model is satisfactory. The prediction results showed that the grade of coal and gas outburst increase with the increase of gas pressure (P) in coal, decrease with the increase of solidity coefficient (f) of coal, increase with the decrease of strength of coal, change little with the gas release coefficient ¿p, integrated indices D and K. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISCID.2009.247 | ISCID (2) |
Keywords | Field | DocType |
neural network,gas pressure,gas release coefficient,artificial neural network model,ann model,backpropagation,gas outburst,prediction performance,gas outburst increase,mining,prediction result,gas outburst prediction,solidity coefficient,back-propagation neural network model,coal outburst prediction,neural nets,data mining,artificial neural network,predictive models,mathematical model,databases,artificial neural networks | Data mining,Artificial neural network model,Solidity,Pattern recognition,Computer science,Coal,Artificial intelligence,Artificial neural network,Backpropagation,Gas pressure,Machine learning | Conference |
Volume | ISBN | Citations |
2 | 978-0-7695-3865-5 | 0 |
PageRank | References | Authors |
0.34 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei You | 1 | 11 | 4.20 |
| Kun Wang | 2 | 36 | 10.73 |
| Huixiao Li | 3 | 1 | 0.69 |
| Yang Jia | 4 | 0 | 0.34 |
| Xiaoqin Wu | 5 | 1 | 0.69 |
| Yaning Du | 6 | 1 | 0.69 |