Transactions of Nonferrous Metals Society of China The Chinese Journal of Nonferrous Metals

您目前所在的位置:首页 - 期刊简介 - 详细页面

中国有色金属学报

ZHONGGUO YOUSEJINSHU XUEBAO

第23卷    第5期    总第170期    2013年5月

[PDF全文下载]        

    

文章编号:1004-0609(2013)05-1427-07
基于拟牛顿法的QN-BP预测爆破振动峰值速度
刘 博,史秀志,黄宣东,武永猛,黄 丹,罗 佳

(中南大学 资源与安全工程学院,长沙 410083)

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%。

 

关键字: 爆破振动;BP神经网络;拟牛顿法;预测

Prediction of blasting-vibration-peak-speed by QN-BP based on Quasi-Newton method
LIU Bo, SHI Xiu-zhi, HUANG Xuan-dong, WU Yong-meng, HUANG Dan, LUO Jia

School of Resources and Safety Engineering, Central South University, Changsha 410083, China

Abstract: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.

 

Key words: blasting vibration; BP neural network; Quasi-Newton method; forecast

ISSN 1004-0609
CN 43-1238/TG
CODEN: ZYJXFK

ISSN 1003-6326
CN 43-1239/TG
CODEN: TNMCEW

主管:中国科学技术协会 主办:中国有色金属学会 承办:中南大学
湘ICP备09001153号 版权所有:《中国有色金属学报》编辑部
------------------------------------------------------------------------------------------
地 址:湖南省长沙市岳麓山中南大学内 邮编:410083
电 话:0731-88876765,88877197,88830410   传真:0731-88877197   电子邮箱:f_ysxb@163.com