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Behavior Categorization Using Correlation Based Adaptive Resonance Theory
Date
2009-06-26
Author
YAVAŞ, mustafa
Alpaslan, Ferda Nur
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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This paper presents a new method of categorizing robot behavior, which is based on a variation of Correlation Based Adaptive Resonance Theory (CobART) learning. CobART is a type of ART 2 network and its main contribution is the usage of correlation analysis methods for category matching. This study uses derivation based correspondence and Euclidian distance as correlation analysis methods for behavior categorization. Tests show that the proposed method generates better results than ART 2 categorization even when a priori SOM (Self-Organizing Map) categorization is combined with ART 2 categorization.
Subject Keywords
Robot behavior recognition
,
Correlation analysis
,
Adaptive resonance theory
URI
https://hdl.handle.net/11511/42722
DOI
https://doi.org/10.1109/med.2009.5164629
Collections
Department of Computer Engineering, Conference / Seminar