A supervised fuzzy adaptive resonance theory with distributed weight update

The Fuzzy Adaptive Resonance Theory is an unsupervised clustering algorithm that solves stability plasticity dilemma The existing winner-take-all approach to updating weights in Fuzzy ART has two flaws: (i) it only updates one cluster while an input might belong to more than one cluster and (ii) the winner-take-all approach is costly in training time since it compares one weight to the input at a time We propose an algorithm that compares all weights to the input simultaneously and allows updating multiple matching clusters that pass the vigilance test To mitigate the effects of possibly updating clusters belonging to the wrong class we introduced weight scaling depending on the “closeness” of the weight to the input In addition, we introduced supervision to penalize the weight update for weights that have the wrong class The results show that our algorithm outperformed original Fuzzy ART in both classification accuracy and time consumption.