A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications
It provides an overview for researchers in engineering applications, but it is incremental as it synthesizes existing models without introducing new methods.
This survey compiles and describes adaptive resonance theory (ART) neural network models for unsupervised, supervised, and reinforcement learning, highlighting their architectures, learning dynamics, and engineering properties like speed and explainability.
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.