主动声纳分类的神经网络

Q4 Computer Science
C. Chen
{"title":"主动声纳分类的神经网络","authors":"C. Chen","doi":"10.1109/ICPR.1992.201812","DOIUrl":null,"url":null,"abstract":"Active sonar classification has been a challenging pattern recognition problem for many years mainly due to the complexity of ocean environment. Improvement of sensors and data acquisition can be very costly and can only provide limited improvement in classification. Neural networks are ideally suited to active sonar classification problems with the potential advantages. In the paper, some active sonar data characteristics are presented, and the performances of several feedforward neural networks are evaluated and compared with the traditional nearest neighbor decision rule. It is concluded that the neural networks studied not only can outperform but also are far more robust than the traditional classifiers.<<ETX>>","PeriodicalId":34917,"journal":{"name":"模式识别与人工智能","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1992-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Neural networks for active sonar classification\",\"authors\":\"C. Chen\",\"doi\":\"10.1109/ICPR.1992.201812\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Active sonar classification has been a challenging pattern recognition problem for many years mainly due to the complexity of ocean environment. Improvement of sensors and data acquisition can be very costly and can only provide limited improvement in classification. Neural networks are ideally suited to active sonar classification problems with the potential advantages. In the paper, some active sonar data characteristics are presented, and the performances of several feedforward neural networks are evaluated and compared with the traditional nearest neighbor decision rule. It is concluded that the neural networks studied not only can outperform but also are far more robust than the traditional classifiers.<<ETX>>\",\"PeriodicalId\":34917,\"journal\":{\"name\":\"模式识别与人工智能\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"模式识别与人工智能\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1992.201812\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"模式识别与人工智能","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/ICPR.1992.201812","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 6

摘要

由于海洋环境的复杂性,多年来主动声呐分类一直是一个具有挑战性的模式识别问题。传感器和数据采集的改进可能非常昂贵,并且只能在分类方面提供有限的改进。神经网络以其潜在的优势非常适合于主动声纳分类问题。本文给出了主动声纳数据的一些特征,并对几种前馈神经网络的性能进行了评价,并与传统的最近邻决策规则进行了比较。研究结果表明,所研究的神经网络不仅性能优于传统的分类器,而且鲁棒性也远高于传统的分类器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural networks for active sonar classification
Active sonar classification has been a challenging pattern recognition problem for many years mainly due to the complexity of ocean environment. Improvement of sensors and data acquisition can be very costly and can only provide limited improvement in classification. Neural networks are ideally suited to active sonar classification problems with the potential advantages. In the paper, some active sonar data characteristics are presented, and the performances of several feedforward neural networks are evaluated and compared with the traditional nearest neighbor decision rule. It is concluded that the neural networks studied not only can outperform but also are far more robust than the traditional classifiers.<>
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
模式识别与人工智能
模式识别与人工智能 Computer Science-Artificial Intelligence
CiteScore
1.60
自引率
0.00%
发文量
3316
期刊介绍:
×
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