Fuzzy Flip-Flops and Neural Nets?

J-K flip-flops are elementary digital units providing sequential features/memory functions. Their definitive equation is used both in the minimal disjunctive and conjunctive forms. Fuzzy connectives do not satisfy all Boolean axioms, thus the fuzzy equivalents of these equations result in two non-equivalent definitions, "reset and set type" fuzzy flip-flops (F³) by Hirota et. al. when introducing the concept of F³ . The paper gives an overview of some of the most famous F³-s by presenting their definitions and graphs. An interesting aspect of F -s might be that they have a certain convergent behavior when one of their inputs (e.g. J) is exited repeatedly. This is true even if the other input (K) is kept at a constant value. The behavior is more versatile if both inputs are given a series of changing values. If J is considered the equivalent of the traditional input of a neuron (with an adder unit applied before J), K might play a secondary modifier\´s role, or can just be set fix. The paper encourages to the investigation of such possible F -networks as new alternative types of neural networks.