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Prediction method of mine gas emission based on complex neural work optimized by Wolf pack algorithm

机译:基于狼包算法优化的复杂神经工作的矿井气体排放预测方法

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摘要

In view of the local extreme problem of the gradient descent algorithm, which makes the working face of mine gas emission prediction uncertainly, this paper combined Wolf pack algorithm (WPA) with complex neural network nonlinear prediction method to the established new prediction model. The WPA shows good global convergence and computational robustness in the solving process of complex high-dimensional functions. Working face in a coal mine as a case, this paper selects seven factors as input variables of the mine gas emission prediction, uses training data to mature prediction model and adopted it to predict six group gas emission data. Research results show that the mean absolute percentage value of the complex neural network model which has been optimized by WPA is 0.06%, the root mean square error value is 0.0191, the mean absolute error value is 0.0175 and the equal coefficient value is 0.9979. The prediction results are very close to the real value, and the change trend is highly consistent with the actual situation.
机译:考虑到与复杂的神经网络的非线性预测方法所建立的新的预测模型中的梯度下降算法,这使得矿井气体排放预测的工作面不确定的局部极值问题,本文结合狼包算法(WPA)的。该WPA表现出良好的全局收敛性和在复杂的高维函数求解过程计算的鲁棒性。在煤矿工作面为的情况下,本文选择七个因素作为瓦斯排放预测的输入变量,使用训练数据以成熟的预测模型,并通过它来预测6组气体排放数据。研究结果表明,其已被WPA优化复杂的神经网络模型的平均绝对百分比值是0.06%,在根均方误差值是0.0191,平均绝对误差值是0.0175和等于系数值为0.9979。预测结果非常接近真实值,变化趋势与实际情况高度一致。

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