Behavior Categorization Using Correlation Based Adaptive Resonance Theory

Date

2009-06-26

Author

YAVAŞ, mustafa
Alpaslan, Ferda Nur

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

49
views

0
downloads

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

Suggestions

OpenMETU
Core

Hierarchical behavior categorization using correlation based adaptive resonance theory Yavaş, Mustafa; Alpaslan, Ferda Nur (2012-02-01) This paper introduces a new model for robot behavior categorization. Correlation based adaptive resonance theory (CobART) networks are integrated hierarchically in order to develop an adequate categorization, and to elicit various behaviors performed by the robot. The proposed model is developed by adding a second layer CobART network which receives first layer CobART network categories as an input, and back-propagates the matching information to the first layer networks. The first layer CobART networks cat...
Hierarchical behavior categorization using correlation based adaptive resonance theory Yavaş, Mustafa; Alpaslan, Ferda Nur; Department of Computer Engineering (2011) This thesis introduces a novel behavior categorization model that can be used for behavior recognition and learning. Correlation Based Adaptive Resonance Theory (CobART) network, which is a kind of self organizing and unsupervised competitive neural network, is developed for this purpose. CobART uses correlation analysis methods for category matching. It has modular and simple architecture. It can be adapted to different categorization tasks by changing the correlation analysis methods used when needed. Cob...
Probabilistic aggregation strategies in swarm robotic systems Soysal, O; Şahin, Erol (2005-06-10) In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performa...
CobART: Correlation Based Adaptive Resonance Theory YAVAŞ, mustafa; Alpaslan, Ferda Nur (2009-06-26) This paper introduces a new type of ART 2 network that performs satisfactory categorization for a domain where the patterns are constructed from consecutive analog inputs. The main contribution relies on the correlation analysis methods used for category-matching. The resulting network model is named as Correlation Based Adaptive Resonance Theory (CobART). Correlation waveform analysis and Euclidian distance methods are used to elicit correlation between the learned categories and the data fed to the network.
Robotic system design for reshaping estimated human intention in human-robot interactions Durdu, Akif; Erkmen, İsmet; Erkmen, Aydan Müşerref; Department of Electrical and Electronics Engineering (2012) This thesis outlines the methodology and experiments associated with the reshaping of human intention via based on the robot movements in Human-Robot Interactions (HRI). Although works on estimating human intentions are quite well known research areas in the literature, reshaping intentions through interactions is a new significant branching in the field of human-robot interaction. In this thesis, we analyze how previously estimated human intentions change based on his/her actions by cooperating with mobile...

Citation Formats

m. YAVAŞ and F. N. Alpaslan, “Behavior Categorization Using Correlation Based Adaptive Resonance Theory,” 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42722.