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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Krieger, A. Brotherton, T. Mears, E. |
| Copyright Year | 1991 |
| Description | Author affiliation: ORINCON Corp., San Diego, CA, USA (Krieger, A.; Brotherton, T.; Mears, E.) |
| Abstract | Two neural net (NN) architectures (single NN and multilayer NN), each performing feature fusion for detection and classification of underwater transient signals, are compared. The impact of the different architectures on the training policies is considered; the results of various training schemes are presented, and an attempt is made to obtain an optimal training policy for each of the competing architectures. It is seen that in some cases a neural net using a single input feature can perform well. However, the results obtained indicate that the fused network is the most robust with respect to different levels of signal with additive noise and across the classes considered. |
| Starting Page | 1119 |
| Ending Page | 1122 |
| File Size | 272118 |
| Page Count | 4 |
| File Format | |
| ISBN | 0818624701 |
| ISSN | 10586393 |
| DOI | 10.1109/ACSSC.1991.186621 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1991-11-04 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Underwater acoustics Acoustic signal processing Biomedical signal processing Acoustic signal detection Event detection Underwater tracking Sonar detection Information filtering Information filters |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Computer Networks and Communications |
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