Analogue circuit design and implementation of an adaptive resonance theory (ART) neural network architecture

1994 ◽  
Vol 76 (2) ◽  
pp. 271-291 ◽  
Author(s):  
CHING S. HO ◽  
JUIN J. LlOU ◽  
MICHAEL GEORGIOPOULOS ◽  
GREGORY L. HEILEMAN ◽  
CHROSTOS CHRISTODOULOU
Keyword(s):  
Neural Network ◽  
Circuit Design ◽  
Network Architecture ◽  
Adaptive Resonance Theory ◽  
Neural Network Architecture ◽  
Resonance Theory ◽  
Analogue Circuit ◽  
Adaptive Resonance ◽  
Design And Implementation

scholarly journals ARTgrid: A Two-Level Learning Architecture Based on Adaptive Resonance Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Marko Švaco ◽  
Bojan Jerbić ◽  
Filip Šuligoj
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Adaptive Resonance Theory ◽  
Neural Network Architecture ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Fuzzy Art ◽  
Search Mechanism ◽  
Robotic Applications ◽  
Computation Speed

This paper proposes a novel neural network architecture based on adaptive resonance theory (ART) called ARTgrid that can perform both online and offline clustering of 2D object structures. The main novelty of the proposed architecture is a two-level categorization and search mechanism that can enhance computation speed while maintaining high performance in cases of higher vigilance values. ARTgrid is developed for specific robotic applications for work in unstructured environments with diverse work objects. For that reason simulations are conducted on random generated data which represents actual manipulation objects, that is, their respective 2D structures. ARTgrid verification is done through comparison in clustering speed with the fuzzy ART algorithm and Adaptive Fuzzy Shadow (AFS) network. Simulation results show that by applying higher vigilance values (ρ>0.85) clustering performance of ARTgrid is considerably better, while lower vigilance values produce comparable results with the original fuzzy ART algorithm.

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