基于RBF神经网络的煤矿瓦斯涌出量预测

Prediction of Mine Gas Emission Quantity Based on Radial Basis Function Neural Network

煤矿瓦斯涌出量预测是矿井安全中的一个关键和热点问题。煤矿瓦斯涌出量涉及很多因素,例如日产量、日进度、煤层厚度、煤层间距、煤层深度等,瓦斯涌出量预测是一个非线性问题。径向基神经网络是目前应用非常广泛的一种局部神经网络模型,在函数回归、序列预测中具有很好的应用效果。文中提出了将径向基神经网络用于预测煤矿瓦斯涌出量的想法,并分析了可行性。

The prediction of mine gas emission quantity is a key and important problem in the mine safety. Coal mine gas emission is related to many factors, such as product per day, day schedule, seam thickness, seam spacing, seam depth, the gas emission prediction is a non-linear problems. Radial basis function neural network is widely used as a local neural network model, and has been successfully applied in the functions regression, series prediction and so on. In this paper, we apply the radial basis function neural network for predicting coal mine gas emission of ideas. The feasibility of the proposed method is analyzed.