刘团结, 赵象卓, 韩永亮, 李云鹏, 陈希. 基于GRA—BP神经网络的固体废弃物充填体强度预测[J]. 煤矿安全, 2021, 52(9): 231-238.
    引用本文: 刘团结, 赵象卓, 韩永亮, 李云鹏, 陈希. 基于GRA—BP神经网络的固体废弃物充填体强度预测[J]. 煤矿安全, 2021, 52(9): 231-238.
    LIU Tuanjie, ZHAO Xiangzhuo, HAN Yongliang, LI Yunpeng, CHEN Xi. Strength prediction of solid wastes filling body based on GRA--BP neural network[J]. Safety in Coal Mines, 2021, 52(9): 231-238.
    Citation: LIU Tuanjie, ZHAO Xiangzhuo, HAN Yongliang, LI Yunpeng, CHEN Xi. Strength prediction of solid wastes filling body based on GRA--BP neural network[J]. Safety in Coal Mines, 2021, 52(9): 231-238.

    基于GRA—BP神经网络的固体废弃物充填体强度预测

    Strength prediction of solid wastes filling body based on GRA--BP neural network

    • 摘要: 为解决煤矿采空区充填材料不足和固体废弃物引起的生态环境问题,研究分析了膏体充填体强度的影响因素,采用正交试验测定了充填体样本强度,通过灰关联分析法确定了各影响因素与充填体强度之间的关联度,用改进的BP神经网络建立了以固体废弃物膏体充填体强度影响因素为输入层节点,充填体强度为输出层节点的强度预测模型;基于正交试验获得的强度试验数据作为网络的训练样本和测试样本,通过对建立的网络进行仿真模拟,检验了网络数据拟合能力和泛化能力。检验结果表明:建立的预测模型收敛速度快而且精度高,网络预测精度达到了93.75%,能够实现对充填体强度的准确预测。

       

      Abstract: In order to solve the problems of ecological environment caused by the shortage of filling materials and solid wastes in the goaf of coal mine, the paper puts forward some suggestions to solve these problems, in the paper, influencing factors of paste filling body strength were analyzed, and sample strength of filling body was determined by orthogonal test, the correlation between the influence factors and the strength of the filling body was determined with grey relational analysis method, and an improved BP strength prediction model was built, in which the factors affecting the strength of solid waste paste filling are input layer and the strength of the filling body is output layer. The strength test data obtained from orthogonal test were used as training samples and test samples of the network, the fitting ability and generalization ability of network data were tested by analog simulation. Test results show that the established prediction model has fast convergence speed and high accuracy, and its prediction accuracy reaches 93.75%, which indicates accurate prediction of the filling body strength can be realized.

       

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