遗传算法与神经网络的结合在煤与瓦斯突出危险性预测中的应用研究  

On the combined application of genetic algorithm and neural network in the prediction of coal/gas outbursts

作　　者：陈全秋[1] 郭勇义[1,2] 吴世跃[1] 徐玉胜[3] 王灿召[1] 

机构地区：[1]太原理工大学矿业工程学院,山西省太原市030024 [2]太原科技大学,山西省太原市030024 [3]晋城煤业集团寺河矿,山西省晋城市048205

出　　处：《中国煤炭》2010年第3期86-89,共4页China Coal

基　　金：国家科技支撑计划项目(2007BAK29B01);山西省科技攻关项目(2007031120-02)

摘　　要：将反映煤与瓦斯突出的重要特征指标:最大钻屑量(S)、钻屑解析指标(K1)、钻孔瓦斯涌出初速度(q0)和煤的坚固性系数(f)通过神经网络与遗传算法有效结合,建立煤与瓦斯突出危险性预测模型,并通过现场实测数据进行突出危险性预测。结果表明:两种算法的结合对煤与瓦斯突出危险性预测是有效的,它与传统的预测方法相比收效速度更快,容错能力更强,预测精度更高。Indicators reflecting the important characters of coal/gas outbursts such as maximum drill chips （S）,drill chip analytical index （K1）,initial velocity of gas outflow through drill hole （q0） and the coefficient of coal rigidity （f） are effectively combined with genetic algorithm via neural network to form a coal/gas outburst prediction model.With the help of the data and test results obtained on site,outburst danger is predicted.Results indicate that the combination of the two algorithms produces an effective prediction of the danger of coal/gas outbursts.Compared with traditional prediction methods,this method produces quicker results with higher fault tolerance and better accuracy.

关 键 词：煤与瓦斯突出 预测 特征指标 神经网络 遗传算法 

分 类 号：TD713[矿业工程—矿井通风与安全]