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Study on the Gas Content of Coal Seam based on the BP Neural Network

机译：基于BP神经网络的煤层气含量研究

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The prediction model for gas content in coal seam has been built based on the BP Neural Network to predict gas content accurately. And the model has been solved and forecasted by combining MATLAB programming with actual data. Moreover, the comparison analysis has been performed with the traditional prediction model based on multiple-regression. The results show that the non-linear gas content model related with basement buried depth and coal seam thickness etc could be established by utilizing the BP Neural Network. And its prediction accuracy and feasibility are better than the multiple-regression model. It is an ideal model for predicting gas content. It could provide some new ideas for the gas content prediction and the prevention and control for coal and gas outburst.
机译：基于BP神经网络建立了煤层气含量预测模型，基于BP神经网络精确地预测气体内容。通过使用实际数据组合MATLAB编程来解决和预测该模型。此外，基于多元回归的传统预测模型进行了比较分析。结果表明，通过利用BP神经网络，可以建立与地下室埋藏深度和煤层厚度相关的非线性气体含量模型。其预测精度和可行性优于多元回归模型。它是预测气体含量的理想模型。它可以为煤气内容预测和煤气爆发的预防和控制提供一些新的思路。

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