# A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

## TL;DR

This survey reviews 30 years of adaptive resonance theory neural network models, highlighting their architectures, learning dynamics, engineering properties, and current challenges to guide researchers in the field.

## Contribution

It provides a comprehensive overview of ART models from classic to modern, detailing their characteristics, properties, and practical applications in engineering.

## Key findings

- ART models support unsupervised, supervised, and reinforcement learning.
- They exhibit distinctive features like code representation and long-term memory.
- ART models are characterized by speed, configurability, and explainability.

## Abstract

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers.