COMPARISON OF SIGNAL RECOGNITION METHODS BY COMBINED USE OF APPROPRIATE EVALUATION CRITERIA WITHIN THE ADDITIVE CONVOLUTION

R. Rzayev, A. Kerimov
{"title":"COMPARISON OF SIGNAL RECOGNITION METHODS BY COMBINED USE OF APPROPRIATE EVALUATION CRITERIA WITHIN THE ADDITIVE CONVOLUTION","authors":"R. Rzayev, A. Kerimov","doi":"10.25045/jpis.v14.i2.03","DOIUrl":null,"url":null,"abstract":"Existing signal recognition methods have both their advantages and disadvantages, which are found when recognizing signals from classes defined by different characteristic standards. Therefore, for signals from different classes, the indicators of recognition quality by one method or another can differ significantly. There is a need to create a more balanced method capable of providing the necessary stability relative to the accuracy and reliability of the final results in the process of recognizing signals from various classes. As such signal recognition method, the article proposes to use an approach based on the combine using of weighted signal proximity criteria within the additive convolution. Euclidean distances between reference points are used as evaluation criteria, which are used in the context of applying the four most well-known recognition methods: the amplitude method (the trivial Euclid method), the DDTW method using the values of the first derivatives, and methods based on the Wavelet transform and the Fourier transform.","PeriodicalId":306024,"journal":{"name":"Problems of Information Society","volume":"17 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Problems of Information Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25045/jpis.v14.i2.03","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Existing signal recognition methods have both their advantages and disadvantages, which are found when recognizing signals from classes defined by different characteristic standards. Therefore, for signals from different classes, the indicators of recognition quality by one method or another can differ significantly. There is a need to create a more balanced method capable of providing the necessary stability relative to the accuracy and reliability of the final results in the process of recognizing signals from various classes. As such signal recognition method, the article proposes to use an approach based on the combine using of weighted signal proximity criteria within the additive convolution. Euclidean distances between reference points are used as evaluation criteria, which are used in the context of applying the four most well-known recognition methods: the amplitude method (the trivial Euclid method), the DDTW method using the values of the first derivatives, and methods based on the Wavelet transform and the Fourier transform.
信号识别方法的比较,结合使用适当的评价标准内加性卷积
在识别由不同特征标准定义的类别信号时,发现现有的信号识别方法各有优缺点。因此,对于不同类别的信号,不同方法的识别质量指标差异很大。有必要创造一种更加平衡的方法,能够在识别来自不同类别的信号的过程中提供相对于最终结果的准确性和可靠性的必要稳定性。对于这样的信号识别方法,本文提出了一种基于加性卷积内加权信号接近准则结合的方法。参考点之间的欧几里得距离被用作评估标准,在应用四种最著名的识别方法的背景下使用:振幅法(平凡欧几里得方法),使用一阶导数值的DDTW方法,以及基于小波变换和傅里叶变换的方法。
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