关键词: 开采沉陷, 深部开采, BP神经网络, 粒子群优化算法, 预测精度

Abstract: In order to improve the overlying strata subsidence prediction accuracy of deep mining,taking a mine in Jilin Province as the study example,taking the parameters of comprehensive strength of overlaying strata,advancing distance of working face and mining thickness,dip angle of coal seam and mining depth as the main influence factors of mining subsidence prediction,among them,selecting the factors of dip angle of coal seam,advancing distance of working face and mining thickness as the input values of the prediction model,and the output value of the prediction model is maximum subsidence value,so as to establish BP neural network mining subsidence prediction model.According to the deficiencies of slow learning speed,weak ability of anti-interference and easily fall into local minimum value of the classical BP neural network model,so,it is optimized by using a improved particle swarm algorithm,therefore,the improved particle swarm optimization BP model is established.The test results show that:①the improved BP model proposed in this paper,which square correlation coefficient (R₂) is 0.932,mean absolute error (e_ME) is 0.195,mean relative error (e_MRE) is 0.082,training time (t) is 21.5 s,mean square error (e_MSE) is 0.067 1;②classical BP neural network model,which which square correlation coefficient (R₂) is 0.802,mean absolute error (e_ME) is 0.605,mean relative error (e_MRE) is 0.255,training time (t) is 30.9 s,mean square error (e_MSE) is 0.0891;③PSO-BP neural network model,which square correlation coefficient (R₂) is 0.825,mean absolute error (e_ME) is 0.382,mean relative error (e_MRE) is 0.216,training time (t) is 23.5 s,mean square error (e_MSE) is 0.078 2.The above study results further show that the training efficiency of the newly proposed model is higher than other two models,the fitting effects is also better than others,what's more,the prediction accuracy of the model is superior to the other two models,which indicated that the model proposed in this paper is help for improving the prediction accuracy of the mining subsidence.

Key words: Mining subsidence, Deep mining, BP neural network, Partial swarm optimization algorithm, Prediction accuracy