煤与瓦斯突出的粗神经网络预测模型研究

New method for predicting coal or gas outburst based on RSNN neural network

将粗集方法作为BP神经网络的前端处理器,通过对煤与瓦斯系统属性特征的提取和影响因素的约简,较好解决了预测输入特征的"维数灾"问题,构建了粗集与神经网络相结合的煤与瓦斯突出预测模型。仿真实验表明,验证了该方法的有效性,模型学习速度更快、精确度更高,对提高瓦斯突出预测时效性有重大意义。

A coal or gas outburst prediction model combining Rough-Set （RS） and BP Artificial Neural Network （ANN） is presented.RS theory is applied in analyzing coal or gas outburst dataset and the dependence relation between geological mining factor and coal or gas outburst is obtained on the basis of these data.So the feature elements are selected from the lager dimensions injections and regarded as ANN injection features,the number of the injection features can be reduced, and the ＂dimensions misfortune＂ problem caused by application of ANN coal or gas outburst prediction method to bulk power system is solved.The actual simulation example demonstrates that the model overcomes the disadvantages of constringency and has fast convergence speed and high prediction accuracy,compared with the single ANN method,and has an important practical meaning for the mine production safety.