基于T-S模糊神经网络的瓦斯涌出量预测
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摘要
在对T-S模糊神经网络原理进行分析的基础上,采用MATLAB语言,利用赵庄二号井2314工作面所测得的数据,建立T-S模糊神经网络预测模型。该方法具有拟合性能强,预测准确度高的优点,可以作为采煤工作面瓦斯涌出量预测的方法,对于瓦斯治理有着重要意义。
In the analysis of t-s fuzzy neural network principle,on the basis of using the MATLAB language,the use of 2314 face Zhao Zhuang no. 2 shaft measured data,t-s fuzzy neural network prediction model is established. This method has a fitting performance is strong,the advantage of high prediction accuracy,can be used as coal working face gas emission prediction,the method of management has important significance for gas.
In the analysis of t-s fuzzy neural network principle,on the basis of using the MATLAB language,the use of 2314 face Zhao Zhuang no. 2 shaft measured data,t-s fuzzy neural network prediction model is established. This method has a fitting performance is strong,the advantage of high prediction accuracy,can be used as coal working face gas emission prediction,the method of management has important significance for gas.
引文
[1]杨智懿,熊亚选.工作面瓦斯涌出量神经网络模型预测研究炭工程[J].煤炭工程,2004(10):73-75.
[2]朱红青,常文杰.回采工作面瓦斯涌出BP神经网络分源预测模型及应用[J].煤炭学报,2007(5):504-508.
[3]董礼.矿井瓦斯涌出量的灰色预测[J].煤炭技术,2003(8):63-65.
[4]王春晓,陈开岩,赵红梅.基于回归分析法的瓦斯涌出量预测[J].煤炭技术,2012(3):116-118.
[5]唐朝伟,何国田.神经网络在采煤工作面瓦斯涌出预测中的应用[J].计算机应用,2007,27(S2):202-204.
[6]陈洋,刘恩,陈大力,等.瓦斯涌出量分源预测法的发展与实践研究[J].煤矿安全,2010(2):73-76.
[7]王连福,王华.基于人工神经网络的多模引信磁探测信号处理[J].探测与控制学报,2005(2):48-54.
[8]杨建刚.人工神经网络实用教程[M].杭州:浙江大学出版社,2001.
[2]朱红青,常文杰.回采工作面瓦斯涌出BP神经网络分源预测模型及应用[J].煤炭学报,2007(5):504-508.
[3]董礼.矿井瓦斯涌出量的灰色预测[J].煤炭技术,2003(8):63-65.
[4]王春晓,陈开岩,赵红梅.基于回归分析法的瓦斯涌出量预测[J].煤炭技术,2012(3):116-118.
[5]唐朝伟,何国田.神经网络在采煤工作面瓦斯涌出预测中的应用[J].计算机应用,2007,27(S2):202-204.
[6]陈洋,刘恩,陈大力,等.瓦斯涌出量分源预测法的发展与实践研究[J].煤矿安全,2010(2):73-76.
[7]王连福,王华.基于人工神经网络的多模引信磁探测信号处理[J].探测与控制学报,2005(2):48-54.
[8]杨建刚.人工神经网络实用教程[M].杭州:浙江大学出版社,2001.