Fuzzy information processing with neural networks

During recent years, fuzzy neural networks have found extensive applications in numerous engineering areas. It is known that the fusion of neural networks and fuzzy logic can overcome their individual drawbacks and benefit from each other's merits. 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 /spl alpha/-level cut representation of fuzzy numbers, we propose a simple neural network-based approach for processing fuzzy information. By numerical simulations, our scheme is illustrated to be capable of coping with fuzzy input and output without a need for new network topology or learning algorithm.