黄金科学技术 ›› 2022, Vol. 30 ›› Issue (2): 272-281.doi: 10.11872/j.issn.1005-2518.2022.02.052
Raoqing XIE(),Jianhong CHEN(),Wenfeng XIAO
摘要:
为提高采场稳定性的预测精度,充分考虑采场稳定性高度非线性和受多因素影响的特点,提出了一种基于NPCA-GA-BP神经网络的采场稳定性预测方法。选择影响采场稳定性的10个指标,运用非线性主成分分析减少指标的维度,提取4个主成分综合指标代替原有的10个指标,简化了神经网络结构,提升了运算速度。利用GA的全局寻优特点优化BP神经网络的权值和阈值,进一步增加了神经网络预测精度。以某矿山实测数据为例,对该预测方法进行验证,对比结果显示:NPCA-GA-BP和GA-BP模型的平均相对误差比BP模型分别降低了10.5%和7.6%,表明通过遗传算法优化BP神经网络可显著提高预测精度;NPCA-GA-BP模型的平均相对误差比GA-BP模型降低了2.9%,表明通过非线性主成分分析减少了变量的维度,提高了预测准确率。研究表明:NPCA-GA-BP预测方法具有更高的采场稳定性预测精度,对实现智慧矿山有一定的指导意义。
中图分类号:
Bi Aorui, Luo Zhengshan, Qiao Wei,et al,2018.Prediction of pipeline inner-corrosion based on principal component analysis and particle swarm optimization-support vector machine[J].Surface Technology,47(9):133-140. | |
Ding S, Zhang Y, Chen J,et al,2013.Research on using genetic algorithms to optimize Elman neural networks [J].Neural Computing and Applications,23(2):293-297. | |
Erten G E, Keser S B, Yavuz M,2021.Grid search pptimised artificial neural network for open stope stability prediction[J].International Journal of Mining, Reclamation and Environment,35(8):600-617. | |
Gong Jian, Hu Nailian, Wang Xiaodong,et al,2015.Stability analysis and rock movement prediction of stope roof below the subsidence area [J].Journal of Mining & Safety Engineering,32(2):337-342. | |
Guan Ziqi, Zhu Yulong, Liu Xiaoguang,et al,2019.Modeling of weld pool illumination based on GA optimizing BP neural network [J].Hot Working Technology,48(7):216-220. | |
Guo Chao, Song Weihua, Wei Wei,2014.Stope roof stability prediction based on both SVM and grid-search method [J].China Safety Science Journal,24(8):31-36. | |
Hu Hongwang, Ye Yicheng, Geng Hongbo,et al,2018.Study on stability evalution of layered deposit goaf based on BP neural network[J].Industrial Minerals & Processing, 47(3):60-63,69. | |
Ilonen J, Kamarainen J K, Lampinen J,2003.Differential evolution training algorithm for feed-forward neural networks[J].Neural Processing Letters,17(1):93-105. | |
Ji Long, Shen Hongfang, Xu Chunchun,et al,2019.Comprehensive evaluation of green super rice varieties based on nonlinear principal component analysis[J].Acta Agronomica Sinica,45(7):982-992. | |
Kang Yu, Wang Xinmin, Zhang Qinli,et al,2015.Application of VW-UM model in stope stability evaluation[J].China Safety Science Journal,25(7):128-134. | |
Lei Dingyou, Ma Qiang, Xu Xinping,et al,2018.Forecasting method of expressway traffic volume based on NPCA and GA-RBF[J].Journal of Traffic and Transportation Engineering,18(3):210-217. | |
Li Qihang, Li Xiaoshuang, Geng Jiabo,et al,2021.FLAC3D numerical simulation of open-pit transformation to underground slope and stability[J].Nonferrous Metals(Mining Section), 73(2):5-10. | |
Li Xiaobei, Dai Xingguo, Wang Xinmin,et al,2015.Prediction of stope roof displacement based on CT-GRNN[J].Mining and Metallurgical Engineering,35(6):30-34. | |
Lima D C L, Limao D O R, Roisenberg M,2016.Optimization of neural networks through grammatical evolution and a genetic algorithm[J].Expert Systems with Applications,56(2):368-384. | |
Ling Biaocan, Peng Suping, Zhang Shenghe,et al,2003.Dynamic engineering classification of stope roof stability [J].Chinese Journal of Rock Mechanics and Engineering,(9):1474-1477. | |
Ning Yulin, Jiang Fanjun, Lin Weixing,2019.Study on stability evaluation and treatment scheme of goaf in Huanggang iron mine[J].Mining Research and Development,39(8):74-77. | |
Oo C T, Sasaoka T, Shimada H,et al,2021.Evaluation of stope stability in underground mine;Hermyingyi(Sn-W deposit)mine in Myanmar [J].Journal of Geoscience and Environment Protection,9(1):107-120. | |
Qiao Shoule, Li Qing, Liang Zhiqiang,et al,2018.Mining field stability analysis based on FLAC3D and Monte Carlo method[J].Mining and Metallurgy,27(4):45-49. | |
Santos J, Ferreira A, Flintsch G,2019.An adaptive hybrid genetic algorithm for pavement management[J].International Journal of Pavement Engineering,20(3):226-286. | |
Shao Liangshan, Xu Bo,2015.KPCA-SVM model for predicting Karst collapse tendency level[J].China Safety Science Journal,25(3):60-65. | |
Wang J, Fang J D, Zhao Y D,2019.Visual prediction of gas diffusion concentration based on regression analysis and BP neural network[J].The Journal of Engineering,(13):19-23. | |
Wang Jie, Luo Zhouquan, Qin Yaguang,et al,2018.Prediction of stope stability based on random forest[J].China Safety Science Journal,28(3):155-160. | |
Wang Zhenhua, Gong Dianyao, Li Guangtao,et al,2018.Bending force prediction model in hot strip rolling based on artificial neural network optimize by genetic algorithm[J].Journal of Northeastern University( Natural Science),39(12):1717-1722. | |
Wei Pengyu, Pan Fucheng, Li Shuai,2018.Study on classification of improved artificial bee colony algorithm to optimi-zation of BP neural network[J].Computer Engineering and Applications,54(10):158-163. | |
Wu Shuliang, Chen Jianhong, Yang Shan,2012.Optimization of bolting scheme based on combination of principal component analysis and BP neural network [J].Chinese Journal of Engineering Design,19(2):150-155. | |
Zhang Fei, Yang Tianhong, Hu Gaojian,2018.Stability evaluation of surrounding rock and parameter optimization of stope under complex stress disturbance[J].Journal of Nor-theastern University(Natural Science),39(5):699-704. | |
Zhang Qinli, Li Xieping, Yang Wei,2013.Optimization of filling slurry ratio in a mine based on back-propagation neural network [J].Journal of Central South University (Science and Technology),44(7):2867-2874. | |
Zhang Siyuan, Bao Yanping, Zhang Chaojie,et al,2017.Prediction model of aluminum consumption with BP neural networks in IF steel production[J].Chinese Journal of Engineering,39(4):511-519. | |
Zhao Guangyuan, Ma Fei,2018.Prediction of dust concentration based on particle swarm optimization BP neural network [J].Measurement & Control Technology,37(6):20-23. | |
Zhao Xingdong, Li Huaibin, Zhang Shujing,et al,2020.Analyzing and controlling the stope stability from open-pit to underground mining in Qinglonggou gold mine[J].Metal Mine,49(4):10-14. | |
毕傲睿,骆正山,乔伟,等,2018.基于主成分和粒子群优化支持向量机的管道内腐蚀[J].表面技术,47(9):133-140. | |
龚剑,胡乃联,王孝东,等,2015.塌陷区下部采场顶板稳定性分析及岩移预测[J].采矿与安全工程学报,32(2):337-342. | |
关子奇,朱玉龙,刘晓光,等,2019.基于GA优化BP神经网络的焊接熔池照度建模[J].热加工工艺,48(7):216-220. | |
郭超,宋卫华,魏威,2014.基于网格搜索—支持向量机的采场顶板稳定性预测[J].中国安全科学学报,24(8):31-36. | |
胡洪旺,叶义成,耿宏波,等,2018.基于BP神经网络的层状矿床采空区稳定性评价研究[J].化工矿物与加工,47(3):60-63,69. | |
纪龙,申红芳,徐春春,等,2019.基于非线性主成分分析的绿色超级稻品种综合评价[J].作物学报,45(7):982-992. | |
康虞,王新民,张钦礼,等,2015.VW-UM模型在采场稳定性评价中得应用[J].中国安全科学学报,25(7):128-134. | |
雷定猷,马强,徐新平,等,2018.基于非线性主成分分析和GA-RBF的高速公路交通量预测方法[J].交通运输工程学报,18(3):210-217. | |
李启航,李小双,耿加波,等,2021.FLAC3D数值模拟露天转地下边坡及采场稳定性研究[J].有色金属(矿山部分),73(2):5-10. | |
李小贝,戴兴国,王新民,等,2015.基于CT-GRNN模型的采场顶板位移预测[J].矿冶工程,35(6):30-34. | |
凌标灿,彭苏萍,张慎河,等,2003.采场顶板稳定性动态工程分类[J].岩石力学与工程学报,(9):1474-1477. | |
甯瑜琳,姜凡均,林卫星,2019.黄岗铁矿采空区稳定性评价及治理方案研究[J].矿业研究与开发,39(8):74-77. | |
乔守乐,卿黎,梁志强,等,2018.基于FLAC3D和Monte Carlo法采场稳定性分析[J].矿冶,27(4):45-49. | |
邵良杉,徐波,2015.岩溶塌陷倾向性等级的KPCA-SVM预测模型[J].中国安全科学学报,25(3):60-65. | |
王杰,罗周全,秦亚光,等,2018.基于随机森林理论的采场稳定性预测研究[J].中国安全科学学报, 28(3):155-160. | |
王振华,龚殿尧,李广焘,等,2018.遗传算法优化神经网络的热轧带钢弯辊力预报模型[J].东北大学学报(自然科学版),39(12):1717-1722. | |
韦鹏宇,潘福成,李帅,2018.改进人工蜂群优化BP神经网络的分类研究[J].计算机工程与应用,54(10):158-163. | |
邬书良,陈建宏,杨珊,2012.基于主成分分析与BP网络的锚杆支护方案优选[J].工程设计学报,19(2):150-155. | |
张飞,杨天鸿,胡高建,2018.复杂应力扰动下围岩稳定性评价与采场参数优化[J].东北大学学报(自然科学版),39(5):699-704. | |
张钦礼,李谢平,杨伟,2013.基于BP网络的某矿山充填料浆配比优化[J].中南大学学报(自然科学版),44(7):2867-2874. | |
张思源,包燕平,张超杰,等,2017.BP神经网络IF钢铝耗的预测模型[J].工程科学学报,39(4):511-519. | |
赵广元,马霏,2018.粒子群算法优化BP神经网络的粉尘浓度预测[J].测控技术,37(6):20-23. | |
赵兴东,李怀宾,张姝婧,等,2020.青龙沟金矿露天转地下采场稳定性分析及控制[J].金属矿山,49(4):10-14. |
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