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

arXiv: https://arxiv.org/abs/1905.11437 · HTML: https://mlsanity.com/paper/1905.11437/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2019.

## Built on by (most cited first, 10 of 10)

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- [Fixing the problems of deep neural networks will require better training data and learning algorithms](https://mlsanity.com/paper/2311.12819/) (2023)
- [Learning inflection classes using Adaptive Resonance Theory](https://mlsanity.com/paper/2512.15551/) (2025)
- [Streaming Adversarial Robustness in Fuzzy ARTMAP: Mechanism-Aligned Evaluation, Progressive Training, and Interpretable Diagnostics](https://mlsanity.com/paper/2605.06902/) (2026)

## Abstract (arXiv)

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.