{"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}
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.<>