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An Improved Coal and Gas Outburst Prediction Algorithm Based on BP Neural Network
| Content Provider | Semantic Scholar |
|---|---|
| Author | Cheng, Li Yan-Ju, Liu Hong-Lie, Zhang |
| Copyright Year | 2015 |
| Abstract | The coal and gas outburst is one of complex geological disasters and its prediction is influenced by a multiple of factors, such as coal gas, ground stress, physical and mechanical properties, and complex non-linear system, which cause the low prediction accuracy. It is a favorable scheme to use the nonlinear BP neural network for the prediction algorithm design. But, the traditional BP neural network algorithm has some defects, such as the slow convergence speed and falling into the local minimum value easily. In order to remedy the defects and improve the prediction accuracy of the coal and gas outburst effectively, the improved BP neural network prediction algorithm of the coal and gas outburst is put forward in this paper. The additional momentum is adopted to adjust the network weight and to speed up the network convergence speed, and then the speed of network learning is adjusted self-adaptively and the number of iterations is reduced. Finally, the simulation of prediction of the coal and gas outburst in mine is carried out. Compared with the traditional BP neural network, the improved algorithm shows its superiorities and provides the basis for the accurate prediction of coal mine disasters. |
| Starting Page | 169 |
| Ending Page | 176 |
| Page Count | 8 |
| File Format | PDF HTM / HTML |
| DOI | 10.14257/ijca.2015.8.6.17 |
| Alternate Webpage(s) | http://www.sersc.org/journals/IJCA/vol8_no6/17.pdf |
| Alternate Webpage(s) | https://doi.org/10.14257/ijca.2015.8.6.17 |
| Volume Number | 8 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
