基于灰色Elman神经网络的煤矿事故预测

Prediction of coal mine accidents based on gray elman neural networks

随着煤炭工业的快速发展,煤矿事故频繁发生,安全生产形势依然严峻。针对煤矿事故的特点,根据我国1998～2007年煤矿事故数据,将灰色预测模型GM(1,1)与Elman神经网络预测模型相结合,建立煤矿事故预测模型。结果表明,灰色Elman神经网络模型优于传统灰色预测模型,符合煤矿事故的特点。由此可对煤矿事故进行科学的预测与分析,为安全管理提供依据,以最大限度地减少事故的发生。

With the rapid development of the coal industry, coal mine accidents occurred frequently, the production safety situation is still grim. Aiming at the characteristics of coal mine accidents, according to China＇s coal mine million tons death rate from 1998 to 2007, by combining the gray model GM（ 1,1 ） and Elman neural networks pre- diction model, the prediction model of coal mine accidents was established. The results showed that the gray Elman neural networks model is superior to the traditional gray prediction model, in line with the characteristics of coal mine accidents. This allowed for a coal mine accidents in a scientific forecast and analysis, so as to provide a basis for safety management in order to minimize accidents.