This paper describes a method integrating neural networks into a system for recognizing underwater objects. The system is based on a combination of simulated dolphin sonar signals, simulated auditory filters and artificial neural networks. The system is tested on a cylinder wall thickness difference experiment and demonstrates high accuracy for small wall thickness differences. Results from the experiment are compared with results obtained by a false killer whale (pseudorca crassidens).
|Title of host publication||Proceedings of the IEEE Workshop on Neural Networks for Signal Processing IX|
|Place of Publication||Piscataway|
|Publication status||Published - 1999|
|Event||1999 IEEE Workshop on Neural Networks for Signal Processing IX - Madison, WI, United States|
Duration: 23 Aug 1999 → 25 Aug 1999
Conference number: 9
|Workshop||1999 IEEE Workshop on Neural Networks for Signal Processing IX|
|Period||23/08/1999 → 25/08/1999|
Bibliographical noteCopyright 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Andersen, L. N., Au, W., Larsen, J., & Hansen, L. K. (1999). Discrimination of Cylinders with Different Wall Thicknesses using Neural Networks and Simulated Dolphin Sonar Signals. In Proceedings of the IEEE Workshop on Neural Networks for Signal Processing IX (pp. 477-486). IEEE. https://doi.org/10.1109/NNSP.1999.788167