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This article presents an hybrid computational model , called Neo-Fuzzy-Neuron Modified by Kohonen Network (NFN-MK),that combines fuzzy system techniques 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 has a similar performance, despite its greater simplicity.
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