|本期目录/Table of Contents|

[1]于瓅,刘泽功.基于神经网络模型的燃料空气混合物爆炸威力预测研究[J].中国安全生产科学技术,2015,11(9):83-87.[doi:10.11731/j.issn.1673-193x.2015.09.013]
　YU Li,LIU Ze-gong.Prediction on explosion power of fuel air mixture based on neural network model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2015,11(9):83-87.[doi:10.11731/j.issn.1673-193x.2015.09.013]
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基于神经网络模型的燃料空气混合物爆炸威力预测研究
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
11
期数:
2015年9期
页码:
83-87
栏目:
职业安全卫生管理与技术
出版日期:
2015-09-30

文章信息/Info

Title:
Prediction on explosion power of fuel air mixture based on neural network model
作者:
于瓅1; 刘泽功2
（1. 安徽理工大学 计算机科学与工程学院, 安徽 淮南 232001;2. 安徽理工大学 能源与安全学院, 安徽 淮南 232001）
Author(s):
YU Li1;  LIU Ze-gong2
1. School of Computer Science and Project, Anhui University of Science and Technology, Huainan Anhui 232001, China;2. School of Mining and Safety Engineering, Anhui University of Science and Technology, Huainan Anhui 232001, China）
关键词:
神经网络; 燃料空气混合物; 爆炸威力; 预测
Keywords:
neural network;  fuel air mixture;  explosion power;  prediction
分类号:
X932
DOI:
10.11731/j.issn.1673-193x.2015.09.013
文献标志码:
A
摘要:
燃料空气混合物爆炸威力准确预测研究是学术界的一个难题。针对燃料空气混合物爆炸威力有效预测问题，采用神经网络方法，设计多层神经网络模型，进行实际预测应用。应用结果表明，采用的预测方法简便、可行，可以为燃料空气混合物爆炸威力预测提供一种新途径。相比3层BP模型，设计的预测模型可以减少训练次数，缩短训练时间，提高预测正确率，应用优势较明显。
Abstract:
The research about accurate prediction on explosion power of fuel air mixture is a difficult problem in academic circle. Aiming at the effective prediction problem on explosion power of fuel air mixture, the neural network method was used to design the multi-layer neural network model, and the practical prediction application was conducted. The results showed that the applied prediction method is simple and feasible, and can provide a new way for the prediction on explosion power of fuel air mixture. Compared with the 3-layer BP model, the prediction model designed in the paper can reduce training number, shorten training time, increase prediction accuracy, and the application advantage is more obvious.

参考文献/References:

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备注/Memo

备注/Memo:
安徽省自然科学基金项目（KJ2011Z086，KJ2013A97）；安徽省博士后基金项目（DG123）；安徽理工大学硕博基金项目（11111）；安徽理工大学中青年学术骨干基金项目
更新日期/Last Update: 2015-09-30