Obtaining Membership Functions from a Neuron Fuzzy System extended by   Kohonen Network
  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. 
Angelo Luis Pagliosa, Claudio Cesar de Sa and Fernando D. Sasse