瓦斯涌出量灰色-RBF网络模型的建立与应用

为了减少瓦斯事故给煤矿生产带来的损失,本文在灰色模型预测煤矿瓦斯涌出量的基础上,结合神经网络理论,构建了灰色-RBF网络模型,充分利用灰色模型的"小样本、贫信息"的预测特点及RBF神经网络自学习、自适应能力特点。首先使用灰色模型对瓦斯涌出量进行初步预测,然后建立RBF网络模型进行再次预测,得到瓦斯涌出量的最终预测值;RBF网络模型的训练和预测计算用MATLAB软件完成。通过对安徽省某矿瓦斯涌出量的预测结果对比,灰色-RBF网络模型的预测误差分别为0.325和0.221,灰色模型预测误差为2.51和2.45,结果表明灰色-RBF网络模型预测明显高于单一灰色模型预测的预测精度。为煤矿瓦斯涌出量预测提供一种预测精度高的方法。

The Establishment and Application of Grey - RBF Network Model for Gas Emission

In order to reduce the loss caused by the gas accident on coal mine production. this paper using the neural network theory based on the grey model to predict the amount of gas emission in the coal mine, the gray -RBF network model was built, it Make full use the predict characteristics of "small sample of the grey model, poor information" and the predict characteristics self-learning and adaptive ability of RBF neural network. First, using the grey model to make a preliminary forecast, next, Radial basis function network model predict again to get the predicted value of the gas emission eventually, The training of the radial basis function network model and forecast calculation was completed with the MATLAB software. The prediction error of Grey - RBF neural network model are 0.325 and 0.221 respectively, the prediction error of gray model are 2.51 and 2.45, the gray-RBF network model prediction has a higher accuracy degree than the single grey model prediction by comparing the prediction results of gas emission from a mine in Anhui Province , therefore, it provides a method of high precision for gas emission prediction in coal mine.