Neural networks-based approximation of fuzzy systems

Abstract

During recent years, fuzzy-neural networks have found extensive applications in numerous engineering areas. It is well known that the fusion of neural networks and fuzzy logic can overcome their individual drawbacks and benefit merits from each other. However, current fuzzy-neural networks often have complex structures and training algorithms. In addition, some of them cannot deal with fuzzy knowledge directly. Inspired by the α-level cut representation of fuzzy numbers, we propose a simple neural networks-based approach to approximating fuzzy rules in this paper. Using numerical simulations, our scheme is illustrated capable of coping with fuzzy input and output without any need for a new network topology or learning algorithm.