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

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dc.creatorPagliosa, Angelo Luis
dc.creatorde Sa, Claudio Cesar
dc.creatorSasse, Fernando D.
dc.date2005-03-29
dc.date.accessioned2026-07-07T03:22:46Z
dc.date.available2026-07-07T03:22:46Z
dc.descriptionThis 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.
dc.description6 pages, 6 figures, 5th Congress of Logic Applied to Technology (LAPTEC 2005) Himeji, Japan, April 2-6, 2005
dc.identifierhttps://arxiv.org/abs/cs/0503078
dc.identifierhttp://arxiv.org/abs/cs/0503078
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32684
dc.subjectNeural and Evolutionary Computing
dc.subjectC.1.3; I.2.6
dc.titleObtaining Membership Functions from a Neuron Fuzzy System extended by Kohonen Network
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