中文摘要：

针对煤矿井下回采工作面瓦斯积聚和瓦斯超限等严重问题，将小波包神经网络模型引入煤矿瓦斯涌出量预测中．首先由改进小波包变化对采集数据进行分解、重构并提取特征向量，然后输入到基于动态节点生成算法的RBF神经网络模型中训练学习，同时采用删除策略简化该模型，最后通过时频联合仿真验证．结果表明，WP-RBF模型在预测精度及训练误差方面明显优于QPSO-RBF模型，是一种非常适合煤矿瓦斯量预测的有效方法．

英文摘要：

According to serious problems such as gas accumulation and gas concentration exceeding limits on the working face underground coal mines, Introducing wavelet packet and neural network model in prediction of mine gas ernission. First, The collected data decomposition, reconstruction and extracted feature vectors by wavelet packet transform, Then input to generation algorithm based on dynamic node RBF neural network model training and learning, While using the simplified model delete policy. Finally, By joint time-frequency simulation, The results show that WP-IRBF model is far superior to QPSO-RBF model in prediction accuracY and training error and is a very suitable for effective method for prediction of coal mine gas quantity.