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Author(s): Carpenter, G.A. | Streilein, W.W. |
Year: 1998
Citation: AeroSense: Proceedings of SPIE s 12th Annual Symposium on Aerospace/Defense Sensing, Simulation, and Control. Orlando, April 13-17, 1998, Bellingham, WA: Society of Photo-Optical Instrumentation Engineers.
Abstract: ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on- line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP- FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.
Topics:
Machine Learning,
Applications:
Remote Sensing,
Models:
ARTMAP,
Modified ART,
