N2 + SF6混合气体击穿电压的预测

S. S. Tezcan, M. Dincer, H. Hiziroglu
{"title":"N2 + SF6混合气体击穿电压的预测","authors":"S. S. Tezcan, M. Dincer, H. Hiziroglu","doi":"10.1109/CEIDP.2006.312101","DOIUrl":null,"url":null,"abstract":"This study proposes artificial neural networks (ANN) to predict the breakdown voltages in N2 + SF6 gas mixtures. The proposed ANN consists of one input layer, two hidden layers and one output layer, which is essentially the predicted breakdown voltage. In order to train the ANN, the experimental data available in literature for N2 + SF6 have been used. When compared with the experimental data the average relative errors on predicted breakdown voltages are found to be less than plusmn5% for training as well as for testing in all cases using the proposed ANNs. Since the average errors are less than 5%, it is recommended to use the proposed ANNs to predict the breakdown voltages.","PeriodicalId":219099,"journal":{"name":"2006 IEEE Conference on Electrical Insulation and Dielectric Phenomena","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Prediction of breakdown voltages in N2 + SF6 gas mixtures\",\"authors\":\"S. S. Tezcan, M. Dincer, H. Hiziroglu\",\"doi\":\"10.1109/CEIDP.2006.312101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study proposes artificial neural networks (ANN) to predict the breakdown voltages in N2 + SF6 gas mixtures. The proposed ANN consists of one input layer, two hidden layers and one output layer, which is essentially the predicted breakdown voltage. In order to train the ANN, the experimental data available in literature for N2 + SF6 have been used. When compared with the experimental data the average relative errors on predicted breakdown voltages are found to be less than plusmn5% for training as well as for testing in all cases using the proposed ANNs. Since the average errors are less than 5%, it is recommended to use the proposed ANNs to predict the breakdown voltages.\",\"PeriodicalId\":219099,\"journal\":{\"name\":\"2006 IEEE Conference on Electrical Insulation and Dielectric Phenomena\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE Conference on Electrical Insulation and Dielectric Phenomena\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEIDP.2006.312101\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Conference on Electrical Insulation and Dielectric Phenomena","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEIDP.2006.312101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

本研究提出人工神经网络(ANN)来预测N2 + SF6混合气体中的击穿电压。所提出的人工神经网络由一个输入层、两个隐藏层和一个输出层组成,输出层本质上是预测的击穿电压。为了训练人工神经网络,我们使用了文献中关于N2 + SF6的实验数据。当与实验数据进行比较时,发现在使用所提出的人工神经网络的所有情况下,对预测击穿电压的平均相对误差小于±5%。由于平均误差小于5%,建议使用所提出的人工神经网络来预测击穿电压。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of breakdown voltages in N2 + SF6 gas mixtures
This study proposes artificial neural networks (ANN) to predict the breakdown voltages in N2 + SF6 gas mixtures. The proposed ANN consists of one input layer, two hidden layers and one output layer, which is essentially the predicted breakdown voltage. In order to train the ANN, the experimental data available in literature for N2 + SF6 have been used. When compared with the experimental data the average relative errors on predicted breakdown voltages are found to be less than plusmn5% for training as well as for testing in all cases using the proposed ANNs. Since the average errors are less than 5%, it is recommended to use the proposed ANNs to predict the breakdown voltages.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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