基于RSNN的煤自燃预测方法

侯媛彬

侯媛彬. 基于RSNN的煤自燃预测方法[J]. 信息与控制, 2004, 33(1): 93-96.
引用本文: 侯媛彬. 基于RSNN的煤自燃预测方法[J]. 信息与控制, 2004, 33(1): 93-96.
HOU Yuan-bin. An RSNN-based Prediction Method for the Coal Mine Spontaneous Combustion[J]. INFORMATION AND CONTROL, 2004, 33(1): 93-96.
Citation: HOU Yuan-bin. An RSNN-based Prediction Method for the Coal Mine Spontaneous Combustion[J]. INFORMATION AND CONTROL, 2004, 33(1): 93-96.

基于RSNN的煤自燃预测方法

详细信息
    作者简介:

    侯媛彬(1953- ),女,博士,教授,博士生导师.研究领域为智能控制理论,安全技术与工程.

  • 中图分类号: TP13

An RSNN-based Prediction Method for the Coal Mine Spontaneous Combustion

  • 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.
  • [1] Zhou L.F,Qian J X.Study on predictive control algorithm for horizon control[A].IEEE World Congress on Intelligent Control and Automation[C].Shanghai:2002.717~721.
    [2] 郭兴明.缓倾特厚综放面煤层自燃预测及防治[D].西安:西安科技学院,1996.52~76.
    [3] 侯媛彬.提高神经网络收敛速度的一种赋初值算法研究[J].模式识别与人工智能,2001,14(4):385~389.
    [4] 侯媛彬,等.粗糙集理论在煤矿皮带运输机故障诊断中的应用研究[J].工矿自动化,2002,(增):26~29.
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出版历程
  • 收稿日期:  2003-01-27
  • 发布日期:  2004-02-19

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