An RSNN-based Prediction Method for the Coal Mine Spontaneous Combustion
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摘要: 本文提出一种基于粗糙集神经网络（Rough Set Neural Network，RSNN）的煤自燃预测方法．该方法针对综放面采空区，在已测到的漏风强度Q和煤体温度TC的基础上，利用RoughSet（RS）的约简理论对测量数据约简．在此基础上构建了一种基于粗糙集的神经网络（RSNN），然后利用该RSNN预测最小浮煤厚度．实测数据验证表明，该方法比常规AMAX预测方法简便且精度高．该方法为基于网络的远程煤矿安全生产监测监控系统奠定了良好的基础．Abstract: A method based on the rough set neural network(RSNN)for the prediction of the coal mine spontaneous combustion is presented in this paper.The measured data is decreased in this way by use of the rough set reduction theory,the data is based on the intensity of the wind leak Q and the temperature of the coal mine TC measured in the mined-out area of the fully mechanized long-wall top-coal caving face.Then the RSNN is established on foundation of the data reduced,and the minimum thickness of the mine layer is predicted using the RSNN.The real-time measured data shows that this method is simpler than the ordinary AMAX prediction method and its precision is high.The method lays a good foundation for the network-based remote coal mine safety monitoring and control system.
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Keywords:
- RSNN /
- the minimum thickness /
- prediction /
- spontaneous combustion
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