基于拟牛顿法的QN-BP预测爆破振动峰值速度  

Prediction of blasting-vibration-peak-speed by QN-BP based on Quasi-Newton method

作　　者：刘博[1] 史秀志[1] 黄宣东[1] 武永猛[1] 黄丹[1] 罗佳[1] 

机构地区：[1]中南大学资源与安全工程学院,长沙410083

出　　处：《中国有色金属学报》2013年第5期1427-1433,共7页The Chinese Journal of Nonferrous Metals

基　　金：中南大学中央高校基本科研业务费专项资金资助项目(2012zzts103);湖南省创新基金研究生探索计划项目(CX2011B119)

摘　　要：根据某露天矿台阶爆破实测数据,利用基于回归分析的经验公式和普通BP神经网络模型以及基于拟牛顿法的改进BP神经网络(QN-BP)模型对爆破振动峰值速度进行预测。两种模型的训练结果表明:QN-BP模型经过122次迭代即可收敛,训练平均误差为3.7%;而普通BP模型收敛需要10万次以上迭代,训练平均误差4.2%。通过QN-BP模型、BP模型和经验公式的预测结果与实测值的对比,三者的平均相对误差分别为6.05%、10.21%和23.42%。According to the measured data of an open pit bench blasting, the experience formula based on regression analysis and ordinary BP neural network model and improved BP neural network model based on Quasi-Newton method （QN-BP） were used to forecast the peak speed of blasting vibration. The training results of two kinds of models show that QN-BP model can be convergence after 122 times iterative, whose average training error is 3.7%. The ordinary BP model need more than 100 000 times iterative to be convergence, whose average training error is 4.2%.By comparing the forecast values with the measured value, the average relative error of the three results（QN-BP, BP and experience formula） are 6.05%, 10.21% and 23.42%, respectively.

关 键 词：爆破振动 BP神经网络 拟牛顿法 预测 

分 类 号：TD235[矿业工程—矿井建设]