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Fusion adaptive resonance theory (fusion ART)[1][2] is a generalization of self-organizing neural networks known as the original Adaptive Resonance Theory[3] models for learning recognition categories across multiple pattern channels. There is a separate stream of work on fusion ARTMAP,[4][5] that extends fuzzy ARTMAP consisting of two fuzzy ART modules connected by an inter-ART map field to an extended architecture consisting of multiple ART modules.
Fusion ART unifies a number of neural model designs and supports a myriad of learning paradigms, notably unsupervised learning, supervised learning, reinforcement learning, multimodal learning, and sequence learning. In addition, various extensions have been developed for domain knowledge integration,[6] memory representation,[7][8] and modelling of high level cognition.
^Tan, A.-H., Carpenter, G. A. & Grossberg, S. (2007) Intelligence Through Interaction: Towards A Unified Theory for Learning . In proceedings, D. Liu et al. (Eds.): International Symposium on Neural Networks (ISNN'07), LNCS 4491, Part I, pp. 1098-1107.
^Tan, A.-H.; Subagdja, B.; Wang, D.; Meng, L. (2019). "Self-organizing neural networks for universal learning and multimodal memory encoding". Neural Networks. 120: 58–73. doi:10.1016/j.neunet.2019.08.020. PMID 31537437. S2CID 202703163.
^Carpenter, G.A. & Grossberg, S. (2003), Adaptive Resonance Theory Archived 2006-05-19 at the Wayback Machine, In Michael A. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks, Second Edition (pp. 87-90). Cambridge, MA: MIT Press
^Y.R. Asfour, G.A. Carpenter, S. Grossberg, and G.W. Lesher. (1993) Fusion ARTMAP: an adaptive fuzzy network for multi-channel classification. In Proceedings of the Third International Conference on Industrial Fuzzy Control and Intelligent Systems (IFIS).
^R.F. Harrison and J.M. Borges. (1995) Fusion ARTMAP: Clarification, Implementation and Developments. Research Report No. 589, Department of Automatic Control and Systems Engineering, The University of Sheffield.
^Teng, T.-H.; Tan, A.-H.; Zurada, J. M. (2015). "Self-Organizing Neural Networks Integrating Domain Knowledge and Reinforcement Learning". IEEE Transactions on Neural Networks and Learning Systems. 26 (5): 889–902. doi:10.1109/TNNLS.2014.2327636. ISSN 2162-237X. PMID 25881365. S2CID 4664197.
^Wang, W.; Subagdja, B.; Tan, A.-H.; Starzyk, J. A. (2012). "Neural Modeling of Episodic Memory: Encoding, Retrieval, and Forgetting". IEEE Transactions on Neural Networks and Learning Systems. 23 (10): 1574–1586. doi:10.1109/TNNLS.2012.2208477. ISSN 2162-237X. PMID 24808003. S2CID 1337309.
^Wang, W.; Tan, A.-H.; Teow, L.-N. (2017). "Semantic Memory Modeling and Memory Interaction in Learning Agents". IEEE Transactions on Systems, Man, and Cybernetics: Systems. 47 (11): 2882–2895. doi:10.1109/TSMC.2016.2531683. ISSN 2168-2216. S2CID 12768875.
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