Comparison of neural network algorithms based on gas qualitative analysis

Yu Mingyan, Shi Yunbo, Z. Wenjie, Feng Qiaohua, Wang Xuan, S. Li-ning
{"title":"Comparison of neural network algorithms based on gas qualitative analysis","authors":"Yu Mingyan, Shi Yunbo, Z. Wenjie, Feng Qiaohua, Wang Xuan, S. Li-ning","doi":"10.1109/IFOST.2011.6021230","DOIUrl":null,"url":null,"abstract":"For the problem of gas qualitatively identify in the field of gas detection, this paper is based on the multi-sensor and pattern recognition of neural network, the uniform change voltage of the sensor output is simulated by the gradient descent algorithm, the additional momentum algorithm and the LM algorithm of neural network, then compare the three simulation results of the three algorithms, the result proves that the LM algorithm is the optimal algorithm of the data simulation in this paper, in the range of allowable error, completed the gas qualitative identification.","PeriodicalId":20466,"journal":{"name":"Proceedings of 2011 6th International Forum on Strategic Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 2011 6th International Forum on Strategic Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IFOST.2011.6021230","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

For the problem of gas qualitatively identify in the field of gas detection, this paper is based on the multi-sensor and pattern recognition of neural network, the uniform change voltage of the sensor output is simulated by the gradient descent algorithm, the additional momentum algorithm and the LM algorithm of neural network, then compare the three simulation results of the three algorithms, the result proves that the LM algorithm is the optimal algorithm of the data simulation in this paper, in the range of allowable error, completed the gas qualitative identification.
基于气体定性分析的神经网络算法比较
针对气体检测领域中的气体定性识别问题,本文基于神经网络的多传感器和模式识别,采用梯度下降算法、附加动量算法和神经网络的LM算法对传感器输出电压的均匀变化进行了模拟,然后对比了三种算法的三种仿真结果,结果证明LM算法是本文数据仿真的最优算法。在允许误差范围内,完成了气体定性鉴定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信