Obtenção das funções de pertinência de um sistema neurofuzzy modificado pela rede de Kohonen

AUTOR(ES)
DATA DE PUBLICAÇÃO

2003

RESUMO

This dissertation presents an hybrid computational model that combines fuzzy system techniques and artificial neural networks. Its objective is the automatic generation of membership functions, in particular, triangle forms, aiming at a dynamic modelling of a system. The model is named Neo-Fuzzy-Neuron Modify by Kohonen (NFN-MK), since it starts using Kohonen network to obtain the central vertices in triangular curves. A set of these curves are used to model a variable of the real system. NFN-MK is based on the Neo-Fuzzy- Neuron (NFN) model originally proposed by Yamakawa, where a network is adapted in order to associate fuzzy, "if-then"rules allowing elicitation and extraction of knowledge in linguistic form. The NFN-MK model is tested by simulation of real systems. They are here represented by classical mathematical functions, chosen due their importance in the system identification field. Finally, a comparison of the results obtained by NFN-MK is carried out against other models such as analytical results, traditional neural networks, and correlated studies of neurofuzzy systems applied to system identification. This work ends with a comparison of the results obtained by NFN-MK with analytical results, and those obtained by using traditional neural networks and other system identification neurofuzzy methods.

ASSUNTO(S)

redes neurais (computação) sistemas fuzzy automação método de kohonen inteligência artificial automacao eletronica de processos eletricos e industriais sistemas neurofuzzy

ACESSO AO ARTIGO

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