This paper formulates a fuzzy logic neuron that uses n-uninorms to construct uni-
nullneurons. A fuzzy neural network (FNN) composed of these neurons is easy to operate
with nullnorms and uninorms at different times, which results in high accuracy of the model
outputs and increases the flexibility in connecting the rule antecedents (enabling AND and
OR connections within one rule). This, in turn, may allow experts/operators to extract more
knowledge from data. The FNN uses a one-versus-rest classifier learning scheme for multi …