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Intelligent Reconfigurable Universal Fuzzy Flip-Flop

Title	Intelligent Reconfigurable Universal Fuzzy Flip-Flop
Publication Type	Web Article
Year of Publication	2010
Authors	Koshak, E., A. Noore, and R. Lovassy
Series Title	IEICE Electronics Express
Edition	7/15
Publication Language	English
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 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.