Table of Contents
Advances in Artificial Neural Systems
Volume 2014, Article ID 185492, 9 pages
http://dx.doi.org/10.1155/2014/185492
Research Article

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

Faculty of Mechanical Engineering and Naval Architecture, Ivana Lučića 5, 10000 Zagreb, Croatia

Received 31 August 2014; Accepted 17 November 2014; Published 3 December 2014

Academic Editor: Ozgur Kisi

Copyright © 2014 Marko Švaco et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [2 citations]

The following is the list of published articles that have cited the current article.

  • Marko Švaco, Bojan Jerbić, Mateo Polančec, and Filip Šuligoj, “A Reinforcement Learning Based Algorithm for Robot Action Planning,” Advances in Service and Industrial Robotics, vol. 67, pp. 493–503, 2018. View at Publisher · View at Google Scholar
  • Leonardo Enzo Brito da Silva, Islam Elnabarawy, and Donald C. Wunsch, “Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence,” Neural Networks, 2019. View at Publisher · View at Google Scholar