ELM模型在煤矿瓦斯事故预警案例推理中的应用探讨

Application of ELM Model in Case Reasoning of Coal Mine Gas Accident Forewarning

分析了煤矿瓦斯事故危险源和案例推理的本质,介绍了基于超限学习机ELM模型的预警方法和步骤。以煤矿瓦斯事故为例,讨论了特征变量与特征参数的选择问题,分析了基于ELM模型在事故预警案例推理中的应用合理性。以支持向量机SVM与BP神经网络模型为参照,仿真实验结果对比表明,基于ELM模型的预警准确率高于BP神经网络模型和SVM模型,验证了ELM模型在早期事故预警中的准确性,认为ELM模型应用于煤矿安全生产早期事故预警是可行的。

The paper analyzed the nature of hazard and case reasoning of coal mine gas accident,introduced the early warning methods and steps of the ELM model which was an extreme learning machine.Taking the coal mine gas accident as an example,the selection of characteristic variables and characteristic parameters was discussed,and the rationality of the application of ELM model in the case reasoning of accident forewarning was analyzed.Simulation results which use SVM and BP neural network model as reference showed that the accuracy of forewarning based on ELM model is higher than that of BP neural network model and SVM model,the accuracy of ELM model in forewarning is verified.It is feasible to apply ELM model to forewarning of coal mine safety in production.