A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications
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 contemporary 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.
L. E. Brito da Silva et al., "A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications," Neural Networks, vol. 120, pp. 167 - 203, Elsevier Ltd, Dec 2019.
The definitive version is available at https://doi.org/10.1016/j.neunet.2019.09.012
Electrical and Computer Engineering
Center for High Performance Computing Research
Keywords and Phrases
Adaptive Resonance Theory; Classification; Clustering; Regression; Reinforcement Learning; Survey
International Standard Serial Number (ISSN)
Article - Journal
© 2019 Elsevier Ltd, All rights reserved.
01 Dec 2019