黄金科学技术 ›› 2024, Vol. 32 ›› Issue (1): 75-81.doi: 10.11872/j.issn.1005-2518.2024.01.108
Changwei ZHOU(),Xianping XIE(),Xidong DU
摘要:
为了准确预测矿山独头巷道CO浓度,基于云南老厂锡矿1800运输巷甩车场独头巷道掘进工作面CO浓度监测数据,运用MATLAB曲线拟合工具箱对该独头巷道中CO浓度随时间的变化情况进行曲线拟合,建立了该矿山独头巷道中CO浓度随时间变化的数学模型。通过该模型得到该独头巷道中CO浓度值达到安全规程要求所需的时间。然后,运用卷积神经网络时间序列预测模型(CNN模型)和BP神经网络时间序列预测模型(BP模型)对独头巷道CO浓度进行预测,并比较评价指标R2和RMSE。结果表明:BP神经网络时间序列预测模型对该独头巷道CO浓度的预测效果更好,为该矿山独头巷道CO浓度值的监测和控制提供了准确可靠的理论依据。
中图分类号:
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