  This article presents the Neo-Fuzzy-Neuron Modified by Kohonen Network
(NFN-MK), an hybrid computational model that combines fuzzy system technique
and artificial neural networks. Its main task consists in the automatic
generation of membership functions, in particular, triangle forms, aiming a
dynamic modeling of a system. The model is tested by simulating real systems,
here represented by a nonlinear mathematical function. Comparison with the
results obtained by traditional neural networks, and correlated studies of
neurofuzzy systems applied in system identification area, shows that the NFN-MK
model has a similar performance, despite its greater simplicity.
