基于SOM-RBF算法的瓦斯涌出量动态预测模型研究

针对煤矿瓦斯涌出量的多影响因素预测问题，以多传感器的瓦斯监测系统采集处理后的数据作为样本，提出了一种自组织特征映射神经网络（Self-organizing Feature Maps，SOM）与多变量的径向基函数(Radial Basis Function，RBF)结合的组合人工神经网络的模型动态预测新方法。采用先聚类、再分类建模和预测的方法，解决了由于训练样本有限和训练样本点分散所导致的预测精度降低的问题，并通过矿井监测到的各项历史数据进行试验。结果表明，与其他预测模型相比较，该模型的预测精度更高，泛化能力更强。预测平均相对误差为2.16%，均相对变动值ARV为0.0059，均方根误差RMSE为0.1311，有效地实现了对煤矿绝对瓦斯涌出量的动态预测，有较高的实用价值。

Gas emission quantity dynamic prediction model of coal mine based on SOM-RBF algorithm

A new model dynamic prediction method of combined artificial neural network combining self-organizing feature maps and multi-variable radial basis function is presented,which adopts collecting and processing data by multi-sensor gas monitoring system as samples,as a solution of the multi-factor prediction problem of coal mine gas emission. The modeling and prediction method are utilized as clustering firstly,and then it is utilized as classification to solve prediction accuracy loss,which is caused by the number limitation of training samples and their dispersion. The presented method is tested on the historical data monitored in the mine,and simulation results show that,the pre?sented model has a higher prediction accuracy and a better performance of generalization with average prediction er?ror 2.16%in comparison with other prediction models,and then average relation variance is 0.005 9 and root-mean-square error is 0.131 1. Therefore,it can be approved that the presented model realizes the dynamic prediction of ab?solute emission quantity of coal mine gas effectively and has a relatively high practicality.