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Obtaining Membership Functions from a Neuron Fuzzy System extended by Kohonen Network

Angelo Luis Pagliosa·Claudio Cesar de Sa·Fernando D. Sasse
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Abstract

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.

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Angelo Luis Pagliosa, Claudio Cesar de Sa, Fernando D. Sasse. 2005-03-29. Obtaining Membership Functions from a Neuron Fuzzy System extended by Kohonen Network. https://arxiv.org/abs/cs/0503078

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