Abstract
In this paper a universal fuzzy flip-flop is proposed that can be reconfigured as a fuzzy SR, D, JK, or T flip-flop. When integrated with a multi layer neural network, the resulting reconfigurable fuzzy-neural structure showed excellent learning ability. The sigmoid activation function of neurons in the hidden layers of the multilayer neural network was replaced by the quasi-sigmoidal transfer characteristics of the universal fuzzy flip-flop in the reconfigurable fuzzy-neural structure. Experimental results showed that the reconfigurable fuzzy-neural structure can be effectively trained using either a large or sparse set of data points to closely approximate nonlinear input functions.