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基于PSO-BP神经网络的导水裂隙带高度预测
引用本文:娄高中,谭毅. 基于PSO-BP神经网络的导水裂隙带高度预测[J]. 煤田地质与勘探, 2021, 49(4): 198-204. DOI: 10.3969/j.issn.1001-1986.2021.04.024
作者姓名:娄高中  谭毅
作者单位:安阳工学院 土木与建筑工程学院,河南 安阳 455000;河南理工大学 能源科学与工程学院, 河南 焦作 454003;煤炭安全生产与清洁高效利用省部共建协同创新中心,河南 焦作 454003
基金项目:国家自然科学基金项目51774111河南省科技攻关项目212102310406安阳工学院博士科研基金项目BSJ2019028
摘    要:导水裂隙带高度是西部矿区保水采煤的理论依据和关键参数。近年来,BP神经网络广泛应用于导水裂隙带高度预测,但BP神经网络存在收敛速度慢、易陷入局部极小等问题。为提高导水裂隙带高度预测的准确性,利用粒子群优化算法(PSO)对BP神经网络的权值和阈值进行优化,建立基于PSO-BP神经网络的导水裂隙带高度预测模型。选择开采厚度、开采深度、工作面倾斜长度、煤层倾角、覆岩结构特征为导水裂隙带高度主要影响因素,选取22例导水裂隙带高度实测数据对PSO-BP神经网络进行训练,将训练后的PSO-BP神经网络对2例测试样本的预测结果与实际值进行对比,并与BP神经网络预测模型及经验公式预测结果进行对比。结果表明:PSO-BP神经网络预测模型的平均相对误差为1.55%;BP神经网络预测模型的平均相对误差为4.8%,经验公式的最小相对误差为9.4%,PSO-BP神经网络预测精度明显优于BP神经网络和经验公式,且绝对误差和相对误差变化较稳定,可以有效预测导水裂隙带高度。 

关 键 词:粒子群优化算法  BP神经网络  导水裂隙带高度  影响因素  预测模型
收稿时间:2021-02-05

Prediction of the height of water flowing fractured zone based on PSO-BP neural network
Abstract:The height of water flowing fractured zone is the theoretical basis and key parameter of water-preserved mining in western mining areas of China. In recent years, BP neural network has been widely used to predict the height of water flowing fracture zone, but it has such defects as slow convergence speed and a tendency to fall into local minimum. In order to improve the prediction accuracy of the height of water flowing fractured zone, the weight values and thresholds of BP neural network were optimized by particle swarm optimization(PSO), and a prediction model was established based on PSO-BP neural network. Mining thickness, mining depth, inclined length of working face, dip angle of coal seam, overburden structural characteristics were chosen as the main influential factors of the height of water flowing fractured zone, and 22 measured data of the height of water flowing fractured zone were selected to train PSO-BP neural network. Then the trained PSO-BP neural network was used to predict two test samples, and the results were compared with the actual values, and with the predicting results of BP neural network prediction model and empirical formulas. The research results show that the average relative error of PSO-BP neural network prediction model is 1.55%, and that of BP neural network prediction model and the minimum relative error of empirical formulas are 4.8% and 9.4% respectively. The prediction accuracy of PSO-BP neural network is obviously significantly better than BP neural network and empirical formulas, and the variation of its absolute error and relative error are relatively stable, so PSO-BP neural network can effectively predict the height of water flowing fractured zone. 
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