Research on SOFMNN in Coal and Gas Outburst Safety Prediction

Title:Research on SOFMNN in Coal and Gas Outburst Safety Prediction

Volume: 9 Issue: 1

Author(s): Hong-Bin Guo, Xiao-Guang Yue, Ying Lu, Tarita Memonen, Fuyuan Xiao and Maia V. Cañiv

Affiliation:

Keywords: Coal and gas outburst, intelligent algorithm, self organizing feature mapping neural network, safety engineering.

Abstract: The mechanism of coal and gas outburst is still controversial, many occurrences of accidents have been a serious threat to people's life and property safety. In order to reduce the effect of coal and gas outburst disaster, the prediction of coal and gas outburst situation has a practical significance. Patent describing intelligent algorithms have been successfully applied in the prediction of coal and gas outburst. The self organizing feature mapping neural network is a high efficiency algorithm mechanism. Based on a comparison with the self organizing feature mapping neural network and improved colony clustering method, the accuracy rate of new algorithm is higher than the old method. This research is good for safety engineering development. The experiments performed demonstrate the effectiveness of the algorithm; this method has reference significance for the prediction of coal and gas outburst.

Cite this article as:

Guo Hong-Bin, Yue Xiao-Guang, Lu Ying, Memonen Tarita, Xiao Fuyuan and V. Cañiv Maia, Research on SOFMNN in Coal and Gas Outburst Safety Prediction, Recent Patents on Computer Science 2016; 9 (1) . https://dx.doi.org/10.2174/2213275908666150422233601