This paper briefly summarizes some valuable properties of ART architectures that are
advantageous in engineering applications, and outlines some areas of likely future …

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction
have been applied in a variety of areas. Applications include airplane design and …

This paper provides an overview of the family of ART (Adaptive Resonance Theory) neural
networks. Theoretical backgrounds of these networks can be found in the numerou s …

This paper introduces the concept of a modular neural network structure, which is capable of
clustering input patterns through unsupervised learning, and representing a self-consistent …

This paper introduces a new class of predictive ART architectures, called Adaptive
Resonance Associative Map (ARAM) which performs rapid, yet stable heteroassociative …

Abstract ART (Adaptive Resonance Theory) neural networks for fast, stable learning and
prediction have been applied in a variety of areas. Applications include airplane design and …

A collection of results related to learning in ART1 networks is presented. These results are
concerned primarily with the complexity of the learning process, rather than with the quality …

Adaptive resonance theory (ART) is a neural-network based clustering method developed
by GA Carpenter and S. Grossberg (1987). Its inspiration is neurobiological and its …

Adaptive resonance theory (ART) describes a family of self-organizing neural networks,
capable of clustering arbitrary sequences of input patterns into stable recognition codes …

ART neural networks are important tools for online supervised pattern recognition. They use
internal categories with pre-defined geometry, given by the category choice function. Pre …