|本期目录/Table of Contents|

[1]聂娜娜,王英博,王铭泽,等.FOA优化GRNN网络的尾矿库安全预测[J].中国安全生产科学技术,2014,10(8):39-45.[doi:10.11731/j.issn.1673-193x.2014.08.007]
　NIE Na-na,WANG Ying-bo,WANG Ming-ze,et al.Safety prediction of mine tailing reservoir based on FOA-GRNN algorithm[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(8):39-45.[doi:10.11731/j.issn.1673-193x.2014.08.007]
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FOA优化GRNN网络的尾矿库安全预测
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
10
期数:
2014年8期
页码:
39-45
栏目:
学术论著
出版日期:
2014-08-31

文章信息/Info

Title:
Safety prediction of mine tailing reservoir based on FOA-GRNN algorithm
作者:
聂娜娜1; 王英博1; 王铭泽2; 李仲学3
(1 辽宁工程技术大学 软件学院, 辽宁 葫芦岛 125105；2 中央财经大学 会计学院, 北京 100081；3 北京科技大学 金属矿山高校开采与安全教育部重点实验室, 北京 100083)
Author(s):
NIE Na-na1; WANG Ying-bo1;  WANG Ming-ze2;  LI Zhong-xue3
（1.College of Software, Liaoning Technical University, Huludao, Liaoning 125105, China;  2. School of Accountancy, Central University of Finance and Economics, Beijing 100081, China; 3. Key Laboratory of Ministry of Education on Safety and Efficient Mining of Metal Mines, Beijing University of  Science and Technology, Beijing 100083, China）
关键词:
尾矿库; 果蝇优化算法; 广义回归神经网络; 安全预测
Keywords:
tailing reservoir;  FOA;  GRNN;  safety prediction
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2014.08.007
文献标志码:
A
摘要:
从尾矿库安全管理实际出发，针对尾矿库安全预测影响因素多、波动性大和非线性的特点，提出了果蝇算法优化广义回归神经网络的尾矿库安全预测模型。通过利用果蝇优化算法的全局寻优特性对广义回归神经网络进行参数优化，同时采用相关分析方法选取尾矿库安全评价指标，实现尾矿库的安全评价预测。以辽宁本溪南芬尾矿库为研究实例进行预测仿真，实验结果表明：相较于GRNN网络模型和BP网络模型，采用果蝇算法优化的GRNN模型预测精度更高，适用性更强，在尾矿库安全预测方面具有很大的实际应用价值。
Abstract:
According to the actual situation of safety management on tailing reservoir, and aiming at the characteristics in safety prediction of tailing reservoir with many influencing factors, stochastic fluctuation and nonlinear, a safety prediction model for tailing reservoir was put forward by using generalized regression neural network optimized by fruit fly optimization algorithm. By using the global optimization characteristic of FOA for GRNN parameters optimization, the correlation analysis method was applied to select the safety evaluation indexes of mine tailings to achieve forecast. Taking Liaoning Benxi Nanfen tailing reservoir as research instance to conduct forecast and simulation, it showed that comparing with model of GRNN network and BP network, FOAGRNN model has higher prediction precision and stronger applicability, and it has great practical application value in safety forecast of tailing reservoir.

参考文献/References:

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备注/Memo

备注/Memo:
国家科技支撑计划项目（2013BAH12F00）；中国煤炭工业科技计划项目基金（MTKJ2009-285）
更新日期/Last Update: 2014-09-26