基于BP神经网络的超前支承压力分布预测  

Distribution Prediction of Advanced Abutment Pressure of Based on BP Neural Network Model

作　　者：李宏亮[1] 华心祝[1,2] 张忠浩[1] 

机构地区：[1]安徽理工大学能源与安全学院,安徽淮南232001 [2]煤矿安全高效开采省部共建教育部重点实验室,安徽淮南232001

出　　处：《煤矿安全》2015年第2期209-212,共4页Safety in Coal Mines

摘　　要：通过对综采工作面前方煤体支承压力分布规律基础上的分析,采用非线性理论分析的神经网络对综采工作面超前支承压力分布进行预测,选取开采深度、煤层采高、煤层倾角、工作面长度、煤体强度、岩层稳定性及覆岩结构7个主要影响因素,构建基于BP神经网络的超前支承压力的预测模型。结果表明:该模型给出的预测值与现场实测数据吻合度较好,误差值均在可接受范围。Based on the analysis of the distribution laws of abutment pressure at the working face,we predict the law of abutment pressure using artificial network technology.We selected seven influence factors of abutment pressure including mining depth,mining height,coal seam inclination angle,workface inclined length,coal body strength,strata stability,and structure of overlying strata.The law of abutment pressure forecast model was established based on artificial neural network.Result shows that actual measurement results and predictive values of the model have goodness of fit,and the error is in the acceptable range.

关 键 词：BP神经网络 支承压力 覆岩结构 预测模型 

分 类 号：TD322[矿业工程—矿井建设]