|本期目录/Table of Contents|

[1]马磊,陆卫东,魏国营.基于GASA-BP神经网络的煤层瓦斯含量预测方法研究*[J].中国安全生产科学技术,2022,18(8):59-65.[doi:10.11731/j.issn.1673-193x.2022.08.009]
 MA Lei,LU Weidong,WEI Guoying.Study on prediction method of coal seam gas content based on GASA-BP neural network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(8):59-65.[doi:10.11731/j.issn.1673-193x.2022.08.009]
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基于GASA-BP神经网络的煤层瓦斯含量预测方法研究*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
18
期数:
2022年8期
页码:
59-65
栏目:
职业安全卫生管理与技术
出版日期:
2022-08-31

文章信息/Info

Title:
Study on prediction method of coal seam gas content based on GASA-BP neural network
文章编号:
1673-193X(2022)-08-0059-07
作者:
马磊陆卫东魏国营
(1.河南理工大学 安全科学与工程学院,河南 焦作 454000;
2.新疆工程学院 安全科学与工程学院,新疆 乌鲁木齐830023;
3.煤炭安全生产与清洁高效利用省部共建协同创新中心,河南 焦作 454000 )
Author(s):
MA Lei LU Weidong WEI Guoying
(1.College of Safety Science and Engineering,Henan Polytechnic University,Jiaozuo Henan 454000,China;
2.Department of Safety Engineering,Xinjiang Institute of Engineering,Urumqi Xinjiang 830023,China;
3.Collaborative Innovation Center of Coal Work Safety and Clean High Efficiency Utilization,Jiaozuo Henan 454000,China)
关键词:
BP神经网络煤层瓦斯含量遗传算法(GA)模拟退火算法(SA)灰色关联分析(GRA)
Keywords:
BP neural network coal seam gas content Genetic Algorithm (GA) Simulated Annealing Algorithm (SA) Grey Relational Analysis (GRA)
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2022.08.009
文献标志码:
A
摘要:
为提高煤层瓦斯含量预测的精准度和效率,提出1种利用遗传算法(GA)和模拟退火算法(SA)混合初始化BP神经网络(BPNN)的瓦斯含量预测新模型(GASA-BPNN模型)。利用灰色关联分析法(GRA)筛选瓦斯含量主控因素并作为GASA-BPNN预测模型的输入。为解决BPNN收敛速度慢和易陷入局部极小陷阱的问题,将GA和具有时变概率突跳性的SA整合为GASA算法协同初始化BPNN的权值和阈值,有效地提高BPNN的参数学习能力。将该模型应用于煤炭生产现场,结果表明:BPNN模型、GA-BPNN模型和GASA-BPNN模型瓦斯含量预测总平均相对误差分别为15.79%,9.03%,5.56%。相比BPNN模型和GA-BPNN模型,GASA-BPNN模型对样本的泛化能力更强,参数训练速度最快并且预测精准度最高。
Abstract:
To improve the accuracy and efficiency of coal seam gas content prediction,a new model of gas content prediction (GASA-BPNN model) was proposed,which used the mixed genetic algorithm (GA) and simulated annealing algorithm (SA) to initialize the BP neural network (BPNN).The grey relational analysis (GRA) method was used to screen the main controlling factors of gas content,which were used as the input of the GASA-BPNN prediction model.To solve the problems of slow convergence speed and easy to fall into the local minimum trap of BPNN,the GA and SA with time-varying probability jump were integrated into the GASA algorithm to initialize the weight and threshold of BPNN,which effectively improved the parameter learning ability of BPNN.The model was applied to the coal production site,and the results showed that the total average relative errors of gas content prediction of BPNN model,GA-BPNN model and GASA-BPNN model were 15.79%,9.03% and 5.56%,respectively.Compared with the BPNN model and GA-BPNN model,the GASA-BPNN model had the stronger generalization ability to the samples,the fastest parameter training speed and the highest prediction accuracy.

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备注/Memo

备注/Memo:
收稿日期: 2021-08-11;网络首发日期: 2022-04-26
* 基金项目: 河南省瓦斯地质与瓦斯治理重点实验室——省部共建国家重点实验室培育基地开放基金项目(WS2018A04);河南省科技攻关项目(202102310221,202102310619)
作者简介: 马磊,硕士研究生,主要研究方向为瓦斯地质与瓦斯治理。
通信作者: 魏国营,博士,教授,主要研究方向为瓦斯地质与瓦斯治理。
更新日期/Last Update: 2022-09-19