A survey of adaptive resonance theory neural network models for engineering applications. (December 2019)

• Record Type: Journal Article
• Authors: Brito da Silva, Leonardo Enzo; Elnabarawy, Islam; Wunsch, Donald C.
• Is Part Of: Neural networks. Volume 120(2019)
• Page Start: 167
• Page End: 203
• Publication Date: 2019-12
• Subjects: Adaptive resonance theory -- Clustering -- Classification -- Regression -- Reinforcement learning -- Survey; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2019.09.012
• Languages: English
• ISSNs: 0893-6080

Abstract

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 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.