DocumentCode
1854512
Title
Classifying sonar returns for the presence of mines: evolving neural networks and evolving rules
Author
Porto, Vincent W. ; Fogel, David B. ; Fogel, Lawrence J. ; Fogel, GaryB ; Johnson, Nathan ; Cheung, Mars
Author_Institution
Natural Selection, Inc., La Jolla, CA
fYear
2005
fDate
March 31 2005-April 1 2005
Firstpage
123
Lastpage
130
Abstract
Sea mines present a danger to commercial shipping, military operations, relief efforts, and can impact a nation´s homeland and economic security. This paper presents results of using evolutionary neural networks or rule-based classifiers to discriminate between active sonar returns from simulated sea mines and other background. The results indicate that both evolutionary methods can be used with considerable success
Keywords
evolutionary computation; neural nets; pattern classification; sonar signal processing; active sonar returns; evolutionary computation; evolutionary neural networks; evolutionary rule-based classifiers; sea mines; sonar return classification; Business; Evolutionary computation; Irrigation; Marine vehicles; Neural networks; Reflection; Reverberation; Sonar detection; Terrorism; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence for Homeland Security and Personal Safety, 2005. CIHSPS 2005. Proceedings of the 2005 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-9176-4
Type
conf
DOI
10.1109/CIHSPS.2005.1500626
Filename
1500626
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