Pub Date : 1991-08-15DOI: 10.1109/ICNN.1991.163322
D. W. Cottle, D. J. Hamilton
The authors present a summary of the status of an ongoing investigation into how effective various neural network paradigms are in military sonar system functions. Specifically, the authors investigate the potential use of neural network technology in the detection, feature extraction, feature optimization, and classification portions of a sonar signal discrimination system. Preliminary results given suggest that neural network techniques have potential as implementation solutions for at least the detection and classification functions.<>
{"title":"All neural network sonar discrimination system","authors":"D. W. Cottle, D. J. Hamilton","doi":"10.1109/ICNN.1991.163322","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163322","url":null,"abstract":"The authors present a summary of the status of an ongoing investigation into how effective various neural network paradigms are in military sonar system functions. Specifically, the authors investigate the potential use of neural network technology in the detection, feature extraction, feature optimization, and classification portions of a sonar signal discrimination system. Preliminary results given suggest that neural network techniques have potential as implementation solutions for at least the detection and classification functions.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116695000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1991-08-01DOI: 10.1109/ICNN.1991.163375
P. Papantoni-Kazakos, D. Kazakos
Fundamental neural network structures in decentralized hypothesis testing are considered. For binary hypothesis testing, the basic neural operations are established, and the Neyman-Pearson criterion is utilized due to information theoretic arguments. Then, two fundamental neural structures are considered, and analyzed and compared in terms of asymptotic performance measures. In particular, the asymptotic relative efficiency performance measure is used to establish performance characteristics and tradeoffs in the two structures, for both parametrically and nonparametrically defined hypotheses. In the latter case, robust neural network structures are considered, and their superiority to parametric network structures is argued.<>
{"title":"Fundamental neural structures, operations, and asymptotic performance criteria in decentralized binary hypothesis testing","authors":"P. Papantoni-Kazakos, D. Kazakos","doi":"10.1109/ICNN.1991.163375","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163375","url":null,"abstract":"Fundamental neural network structures in decentralized hypothesis testing are considered. For binary hypothesis testing, the basic neural operations are established, and the Neyman-Pearson criterion is utilized due to information theoretic arguments. Then, two fundamental neural structures are considered, and analyzed and compared in terms of asymptotic performance measures. In particular, the asymptotic relative efficiency performance measure is used to establish performance characteristics and tradeoffs in the two structures, for both parametrically and nonparametrically defined hypotheses. In the latter case, robust neural network structures are considered, and their superiority to parametric network structures is argued.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133081378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1990-11-01DOI: 10.1109/ICNN.1991.163362
R. L. Greene, R. Field
The goal of the research described was to study the feasibility of using artificial neural networks to recognize (or classify) acoustic transient signals that have been propagated through an ocean environment, including surface and bottom effects. The networks were tested on signals propagated to 25 different receiver sites by the time-domain parabolic equation model. Despite the interference effects from surface and bottom reflections/refractions, the classification accuracy was about 90% in the noise-free case. Classification in the presence of noise is reduced. However, the redundancy provided by the multiple receivers in most cases allows the network to correctly classify all signals from sources on which it was trained. It shows a robustness in the presence of unknown signals not shown by the nearest-neighbor classifier.<>
{"title":"Classification of underwater acoustic transients by artificial neural networks","authors":"R. L. Greene, R. Field","doi":"10.1109/ICNN.1991.163362","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163362","url":null,"abstract":"The goal of the research described was to study the feasibility of using artificial neural networks to recognize (or classify) acoustic transient signals that have been propagated through an ocean environment, including surface and bottom effects. The networks were tested on signals propagated to 25 different receiver sites by the time-domain parabolic equation model. Despite the interference effects from surface and bottom reflections/refractions, the classification accuracy was about 90% in the noise-free case. Classification in the presence of noise is reduced. However, the redundancy provided by the multiple receivers in most cases allows the network to correctly classify all signals from sources on which it was trained. It shows a robustness in the presence of unknown signals not shown by the nearest-neighbor classifier.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129988921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ICNN.1991.163333
W. Gan
Ocean acoustic tomography differs from medical ultrasound tomography and seismic tomography in that one must first understand the forward problem, that is, how the sound channel and the mesoscale feature refracts sound in three dimensions and how such refraction alters the pulse-arrival sequence. The parabolic equation (PE) model is used in the forward problem. A neural network is used to perform the inversion of tomography data. The author uses the feedforward neural network to implement the filtered back projection algorithm. The advantages are that one does not need to assume weak scattering and the instability problem of the frequency domain interpolation algorithm does not exist.<>
{"title":"Applications of neural networks to ocean acoustic tomography","authors":"W. Gan","doi":"10.1109/ICNN.1991.163333","DOIUrl":"https://doi.org/10.1109/ICNN.1991.163333","url":null,"abstract":"Ocean acoustic tomography differs from medical ultrasound tomography and seismic tomography in that one must first understand the forward problem, that is, how the sound channel and the mesoscale feature refracts sound in three dimensions and how such refraction alters the pulse-arrival sequence. The parabolic equation (PE) model is used in the forward problem. A neural network is used to perform the inversion of tomography data. The author uses the feedforward neural network to implement the filtered back projection algorithm. The advantages are that one does not need to assume weak scattering and the instability problem of the frequency domain interpolation algorithm does not exist.<<ETX>>","PeriodicalId":296300,"journal":{"name":"[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130973300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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