| BP神经网络在地表移动边界角预测中的应用 |
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| 引用本文： | 井彦林,郭爱侠,林杜军,孟永会,姜德晟.BP神经网络在地表移动边界角预测中的应用[J].水土保持通报,2009,23(3):193-196. |
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| 作者姓名： | 井彦林 郭爱侠 林杜军 孟永会 姜德晟 |
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| 作者单位： | 井彦林(长安大学建筑工程学院,陕西,西安,710054);郭爱侠,姜德晟(中交第一公路勘察设计研究院有限公司,陕西,西安,710054);林杜军,孟永会(中煤西安设计工程有限责任公司,陕西,西安,710054) |
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| 摘 要： | 在对煤矿采空区地表移动及其边界角影响因素分析的基础上,提出用BP神经网络预测采空区地表移动边界角.建立了边界角预测的BP神经网络模型,利用国内近30项工程的采空区地表移动实测数据作为学习样本对网络进行训练,用实际工程对网络进行了测试分析.结果显示,预测精度平均达90%,研究表明用BP神经网络计算边界角方法可行,具有实用性,为边界角的确定提供了一条新的途径.
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| 关 键 词： | 采空区 边界角 BP神经网络 预测 |
| 收稿时间： | 2008/1/18 0:00:00 |
| 修稿时间： | 2008/11/8 0:00:00 |
A Study on Prediction of Boundary Angle of Surface Movement by BP Artificial Neural Network |
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| JING Yan-lin,GUO Ai-xi,LIN Du-jun,MENG Yong-hui and JIANG De-sheng.A Study on Prediction of Boundary Angle of Surface Movement by BP Artificial Neural Network[J].Bulletin of Soil and Water Conservation,2009,23(3):193-196. |
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| Authors: | JING Yan-lin GUO Ai-xi LIN Du-jun MENG Yong-hui and JIANG De-sheng |
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| Institution: | School of Civil Engineering, Chang'an Unversity, Xi'an, Shaanxi 710061, China;;CCCC First Highway Cosultants Co.Ltd., Xi'an, Shaanxi 710075, China;;China Coal Xi'an Design & Engineering Co.Ltd., Xi'an, Shaanxi 710054, China;China Coal Xi'an Design & Engineering Co.Ltd., Xi'an, Shaanxi 710054, China;CCCC First Highway Cosultants Co.Ltd., Xi'an, Shaanxi 710075, China; |
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| Abstract: | Based on the analysis of influence factors on boundary angle of surface movement in coal minegoaf,this paper presents a method used to predict the boundary angle by the BP Artificial Neural Network.A prediction model of the boundary angle is constructed with BP Artificial Neural Network.Data of the boundary angle from 30 practical engineering projects are used as the assembly of training samples of Neural Network.The prediction model is then validated using data from practical engineering projects.Results from the validation show that prediction precision for the boundary angle is up to 90%,indiicating that the prediction method based based on bp artificial neural network is very useful and feasible in engineering. |
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| Keywords: | goaf boundary BP artificial neural network prediction |
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