Comparison of neofuzzy and rough neural networks

Conventional neural network architectures generally lack semantics. Both rough and neofuzzy neurons introduce semantic structures in the conventional neural network models. Rough neurons make it possible to process data points with a range of values instead of a single precise value. Neofuzzy neurons make it possible to convert crisp values into fuzzy values. This paper compares rough and neofuzzy neural networks. Rough and neofuzzy neurons are demonstrated to be complementary to each other. It is shown that the introduction of rough and fuzzy semantic structures in neural networks can increase the accuracy of predictions.