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Fusion adaptive resonance theory is a generalization of self-organizing neural networks known as the original Adaptive Resonance Theory models for learning recognition categories across multiple pattern channels. There is a separate stream of work on fusion ARTMAP, 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, memory representation, and modelling of high level cognition.
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