An episodal neural-net computing approach to the detection and interpretation of underwater acoustic transients

Yoh Han Pao, Thomas L. Hemminger, Dennis J. Adams, Stuart Clary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Acoustic transients develop and fade away continually in ocean environments. Accordingly, detection and interpretation of these are complicated by the fact that detection and classification cannot be made on the basis of temporal snapshots alone. Interpretation of transients must rest on the processing and classification of entire episodes of such continuing signals. The authors describe experiments in the design and implementation of such an episodal associative classifier which makes concurrent use of neural network self-organization and supervised learning methodologies. This system has no difficulty classifying signals from within test data sets and is also fast, robust, adaptive, and well suited for a wide range of sequence lengths.

Original languageEnglish (US)
Title of host publicationIEEE Conference on Neural Networks for Ocean Engineering
PublisherPubl by IEEE
Pages21-28
Number of pages8
ISBN (Print)0780302052
StatePublished - Dec 1 1991
EventProceedings of the IEEE Conference on Neural Networks for Ocean Engineering - Washington, DC, USA
Duration: Aug 15 1991Aug 17 1991

Publication series

NameIEEE Conference on Neural Networks for Ocean Engineering

Other

OtherProceedings of the IEEE Conference on Neural Networks for Ocean Engineering
CityWashington, DC, USA
Period8/15/918/17/91

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Pao, Y. H., Hemminger, T. L., Adams, D. J., & Clary, S. (1991). An episodal neural-net computing approach to the detection and interpretation of underwater acoustic transients. In IEEE Conference on Neural Networks for Ocean Engineering (pp. 21-28). (IEEE Conference on Neural Networks for Ocean Engineering). Publ by IEEE.