唐山矿瓦斯涌出量动态预测模型

为了提高瓦斯涌出预测的准确性,采用BP型神经网络,利用BP型神经网络自学习、自组织和自适应等特性,在MATLAB环境下构建瓦斯动态预测模型.通过对唐山矿瓦斯信号实时监测数据的分析,对瓦斯动态预测模型进行训练和测试.结果表明,该模型的预测速度快、精度高,可以实现对工作面瓦斯涌出的动态预测,并能综合判断工作面所处地点的安全状况以及前方的潜在的危险性.

Dynamic prediction model of gas emission in Tangshang Mine

To improve the prediction accuracy of gas emission,a BP neural network was applied to establish a dynamic prediction model of gas emission under the MATLAB environment by using BP neural networks’ characteristics of self-learning,self-organizing and self-adapting.The model was trained and tested by analyzing the real-time monitoring data of gas signals from Tangshan Mine.Test results show that the model has higher prediction speed and accuracy.By using the model the dynamic prediction of gas emission in the working face can be realized,the safety state and the potential hazard can be synthetically estimated to provide security for safety production.