|本期目录/Table of Contents|

[1]徐星,郭兵兵,王公忠.人工神经网络在矿井多水源识别中的应用[J].中国安全生产科学技术,2016,12(1):181-185.[doi:10.11731/j.issn.1673-193x.2016.01.034]
　XU Xing,GUO Bingbing,WANG Gongzhong.Application of artificial neural network for recognition of multiple water sources in mine[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2016,12(1):181-185.[doi:10.11731/j.issn.1673-193x.2016.01.034]
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人工神经网络在矿井多水源识别中的应用
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
12
期数:
2016年1期
页码:
181-185
栏目:
现代职业安全卫生管理与技术
出版日期:
2016-01-30

文章信息/Info

Title:
Application of artificial neural network for recognition of multiple water sources in mine
文章编号:
1673-193X（2016）-01-0181-05
作者:
徐星1; 郭兵兵1; 王公忠1; 2
1. 河南工程学院 安全工程学院，河南 郑州 451191；2. 武汉理工大学 资源与环境工程学院，湖北 武汉 430070）
Author(s):
XU Xing1;  GUO Bingbing1;  WANG Gongzhong1; 2
(1. School of Safety Engineering, Henan Institute of Engineering, Zhengzhou Henan 451191, China; 2. School of Resource and Environment Engineering, Wuhan University of Technology, Wuhan Hubei 430070, China)
关键词:
矿井多水源; BP神经网络; Elman神经网络; 识别; 泛化能力
Keywords:
multiple water sources in mine;  BP neural network;  Elman neural network;  recognition;  generalization ability
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2016.01.034
文献标志码:
A
摘要:
以焦作矿区水化数据为例，选用Na++K+、Ca2+、Mg2+、Cl-、SO2-4、HCO-36种水化离子浓度作为识别矿井水源的依据，运用Matlab软件分别建立BP神经网络与Elman神经网络的矿井多水源识别模型。应用结果表明：与静态的BP神经网络相比，在结构上增加承接层的Elman神经网络在训练和仿真中拟合能力更强，识别精度更高和泛化能力更好；矿井地下水随着地下开采与扰动具有动态性，将具有非线性动态特征的Elman神经网络应用于矿井多水源的识别，对准确判断突水来源和分析地下水运移规律具有一定的辅助和指导意义。
Abstract:
Taking the hydration data of Jiaozuo mining area as example, and using the concentrations of six hydrated ion including Na++K+, Ca2+, Mg2+, Cl-, SO2-4 and HCO-3 as the basis for recognition of water sources in mine, the recognition models of multiple water sources in mine were established based on BP neural network and Elman neural network respectively by using Matlab software. The application results showed that compared with the static BP neural network, the fitting ability of Elman neural network which added an undertaking layer in structure in training and simulation was stronger, the recognition accuracy was higher and the generalization ability was better. The mine groundwater has dynamic nature with underground mining and disturbance. It has certain auxiliary and guidance to accurately determine water inrush sources and analyze the migration laws of groundwater by applying the Elman neural network with the nonlinear dynamic characteristics in recognition of multiple water sources in mine.

参考文献/References:

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备注/Memo

备注/Memo:
国家“十二五”科技支撑计划重点项目（2011BAB05B03）；建设经费项目（200925）；2016年度河南省高等学校重点科研项目（16A44001）
更新日期/Last Update: 2016-03-01