| 老采空区残余下沉系数的神经网络模型研究 |
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| 引用本文： | 张宏贞,邓喀中,刘洪义. 老采空区残余下沉系数的神经网络模型研究[J]. 采矿与安全工程学报, 2009, 26(3) |
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| 作者姓名： | 张宏贞 邓喀中 刘洪义 |
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| 作者单位： | 1. 中国矿业大学,环境与测绘学院,江苏,徐州,221116;中国矿业大学,江苏省资源环境,信息工程重点实验室,江苏,徐州,221008
2. 里能里彦矿业有限公司,地测科,山东,邹城,273517 |
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| 基金项目： | 国家自然科学基金项目，中国矿业大学青年教师基金项目，国家"十一五"科技支撑计划重点项目 |
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| 摘 要： | 为了验证人工神经网络方法是否可以用于老采空区残余下沉系数的预测,采用L-M BP算法的人工神经网络方法对某矿区的观测站下沉资料进行了残余下沉系数的预测分析,计算出不同观测时间距最大下沉速度时刻之间的下沉系数,以此建立此下沉系数的神经网络预测模型,进而得到残余下沉系数,并与实测得出的进行对比.研究表明:1) 利用已有观测站最大下沉速度之后的观测数据可以解决数据不足的问题.2) 在考虑地质采矿因素的基础上,建立相对与最大下沉速度时刻的下沉系数的神经网络预测模型,以此求取老采空区残余下沉系数.结果表明:用人工神经网络方法进行老采空区残余下沉系数预测是可行的.
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| 关 键 词： | 老采空区 残余下沉系数 人工神经网络 |
Artificial Neural Network Model for Predicting Residual Subsidence Coefficient of Abandoned Mine Goaf |
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| ZHANG Hong-zhen,DENG Ka-zhong,LIU Hong-yi. Artificial Neural Network Model for Predicting Residual Subsidence Coefficient of Abandoned Mine Goaf[J]. Journal of Mining and Safety Engineering, 2009, 26(3) |
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| Authors: | ZHANG Hong-zhen DENG Ka-zhong LIU Hong-yi |
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| Affiliation: | 1.School of Environment Science and Spatial Informatics;China University of Mining & Technology;Xuzhou;Jiangsu 221116;China;2.Jiangsu Key Laboratory of Resources and Environmental InformationEngineering;Jiangsu 221008;3.Geology and Surveying Office;Lineng Liyan Mining Co.Ltd.;Zoucheng;Shandong 273517;China |
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| Abstract: | To test and verify whether it is practicable to predict residual subsidence coefficient of abandoned mine goaf by ANN,L-M BP algorithm of ANN method was adopted to predict and analyze residual subsidence coefficient based on the observation station data in a mining area,subsidence coefficient between different time and the time of the maximum falling rate was calculated and ANN model for coefficient established.Then the residual subsidence coefficient was obtained and compared with the one calculated by pra... |
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| Keywords: | abandoned mine goaf residual subsidence coefficient artificial neutral networks |
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