COMPARISON OF NEOFUZZY AND ROUGH NEURAL NETWORKS

Pawan Lingras 
Department of Computer Science, Algoma University College
Sault Ste. Marie, Ontario, Canada, P6A 2G4.
e-mail: lingras@tbird.auc.on.ca

ABSTRACT

This paper compares rough and neofuzzy neural networks. The conventional neural network architecture generally lacks semantics. It is shown that both rough and neofuzzy neurons introduce semantic structures in the conventional neural network models. It is shown that the introduction of rough and fuzzy semantic structures in neural networks can increase the accuracy of predictions.