【作者】 念其锋； 施式亮； 李润求；

【Author】 NIAN Qifeng;SHI Shiliang;LI Runqiu;School of Computer Science & Engineering, Hunan University of Science & Technology;Hunan Provincial Key Laboratory of Safe Mining Techniques of Coal Mines, Hunan University of Science & Technology;School of Resource & Safety Engineering, Central Southern University;

【机构】 湖南科技大学计算机科学与工程学院； 湖南科技大学煤矿安全开采技术湖南省重点实验室； 中南大学资源与安全工程学院；

【摘要】 针对煤与瓦斯突出存在诸多不确定影响因素的特点,在研究煤与瓦斯突出机理和广义回归神经网络的基础上,建立了煤与瓦斯突出的广义回归神经网络预测模型,提出了广义回归神经网络中的光滑因子优化选择算法,以提高模型预测精度。通过应用实例验证,预测结果精度高,与实际情况相吻合。研究结果证明了该方法的合理性和可行性,对煤矿提高煤与瓦斯突出区域的预测能力具有较大的参考意义。更多还原

【Abstract】 According to the characteristics of coal and gas outburst that existing many uncertain influencing factors, and studying the theory of the coal and gas outburst and set pair analysis, this paper built the regional forecasting model of generalized regression neural network in coal and gas outburst, and put forward the optimization selection algorithm of smoothing factor in the GRNN to raise the precision of forecasting. By the application example proving, the results had higher precision and were fit to the actual situation. The studying results proved the rationality and feasibility of this method, and it would be more guiding significance to raise the ability of regional forecasting of coal and gas outburst.更多还原