摘要
煤矿的安全事故中有80%以上为瓦斯事故,为了更加准确的预测瓦斯涌出量,使得煤矿安全进一步得到保障,采用足够的具有代表性的瓦斯检测数据作为样本,利用QGA算法优化RBF神经网络的参数,建立了瓦斯涌出量的预测模型,并使用MATLAB进行仿真研究。结果表明,经过优化后的预测模型较单一的RBF网络模型有更好的预测精度,可以为煤矿瓦斯防治提供理论依据。
Safe accidents in coal mine are aroused more than 80 percent by the excess of gas. In order to make gas emission quantity forecasting result more accurate and guarantee the safety of coal mine, detecting sufficient and typical gas data are collected as samples in this paper. The quantum genetic algorithm is adopted to optimize the pa- rameters of Radial Basis Function neural networks, and the forecasting model used for carrying out the gas emission quantity forecasting is established. The simulating result obtained by using Matlab indicates the optimized forecasting model has an more accuracy forecasting result than the forecasting model based on RBF neural networks. A theoretical basis is provided for the prevention and control of gas accidents in coal mine.
出处
《传感技术学报》
CAS
CSCD
北大核心
2012年第1期119-123,共5页
Chinese Journal of Sensors and Actuators
基金
辽宁教育厅高等学校科研计划项目(2009A351)
关键词
QGA算法
RBF神经网络
瓦斯涌出量
无线传感网络
quantum genetic algorithm
radial basis function neural networks
gas emission quantity
wirelesssensor networks
