A neuro-fuzzy system that uses distributed learning for compact rule set generation

ARTMAP based architectures have several desirable properties that make them very suitable for pattern classification problems. However, they suffer from category proliferation. Distributed coding has been proposed as a solution for memory compression and the dARTMAP neural network has been introduced as a modification of the fuzzy ARTMAP that, due to distributed learning, achieves code compression while fast stable learning is retained. A critical analysis of dARTMAP architecture and performance in pattern recognition problems is presented, concluding that distributed learning excels the original fuzzy ARTMAP only under certain geometrical configurations of the output classes, or in the presence of noise in the training set. A new architecture called dFasArt is presented, introducing distributed learning into the FasArt neuro-fuzzy system, which is more suitable for identification tasks, showing that the advantages of distributed code can be extended to other neural architectures. Experimental results show that dFasArt performs similarly to dARTMAP in classification tasks, while being less sensitive to pattern presentation order