A Collection of Art-Family Graphical Simulations
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The Adaptive Resonance Theory (ART) architecture, first proposed by (Grossberg, 1976b, 1976a), is a self-organizing neural network for stable pattern categorization in response to arbitrary input sequences. Since its original formulation, several versions of ART have been proposed, each designed to handle a particular task or input format. Recent ART architectures have been designed to work in a supervised fashion, offering a viable alternative to supervised neural networks such as backpropagation (Rumelhart, Hinton, & Williams, 1986). Perhaps the best-known variant of ART is ART2 (Carpenter & Grossberg, 1987b), an unsupervised neural network that handles analog inputs. We have developed a series of simulators for some of the ART-family neural architectures, namely, ART2 (Carpenter & Grossberg, 1987b), ART2-A (Carpenter, Grossberg, & Rosen, 1991b), Fuzzy ART (Carpenter, Grossberg, & Rosen, 1990), and Fuzzy ARTMAP (Carpenter, Grossberg, Markuzon, & Reynolds, 1992). This article briefly summarizes the history and functionality of ART and its variants, and then describes the software package, which is available in the public domain.