Intelligent Reconfigurable Universal Fuzzy Flip-Flop

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 has excellent learning ability. The sigmoid activation
function of neurons in the hidden layers of the multilayer neural
network is replaced by the quasi-sigmoidal transfer characteristics of
the universal fuzzy flip-flop in the reconfigurable fuzzy-neural structure.
Experimental results show that the reconfigurable fuzzy-neural
structure can be effectively trained using either a large or sparse data
points to closely approximate nonlinear input functions.