A new approach to online training for the Fuzzy ARTMAP artificial neural network

• Autor(a) principal: Santos-Junior, Carlos R.
• Data de Publicação: 2021
• Outros Autores: Abreu, Thays [UNESP], Lopes, Mara L.M. [UNESP], Lotufo, Anna D.P. [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.asoc.2021.107936; http://hdl.handle.net/11449/229705
• topic: Artificial neural networks; Continuous training; Fuzzy ARTMAP; Online learning

The evolution of internet resources has led to an increase in the flow of data and, consequently, the need for classification or forecasting models that support online learning. The Fuzzy ARTMAP neural network has been used in the most areas of knowledge; however, few have explored real-time applications that require continuous training. In this work, a Fuzzy ARTMAP neural network with continuous training is proposed. This new network can acquire knowledge via classification or prediction. Modifications made to the architecture and learning algorithm enable online learning from the first sample of data and perform the classification or forecasting at any time during training. To validate the proposed model, three experiments were performed, one for forecasting and two for classification. Each experiment used benchmark databases and compared its final results with the results of the original Fuzzy ARTMAP neural network. The results demonstrate the ability of the proposed model to acquire knowledge from the first data samples in a stable and efficient way. Thus, this study contributes to the evolution of the Fuzzy ARTMAP neural network and introduces the continuous training method as an effective alternative to real-time applications.