AUXÍLIO À ANÁLISE DE SÉRIES TEMPORAIS NÃO SAZONAIS USANDO REDES NEURAIS NEBULOSAS / IDENTIFICATION OF NON-SEASONAL TIME SERIES THROUGH FUZZY NEURAL NETWORKS

AUTOR(ES)

MARIA AUGUSTA SOARES MACHADO

DATA DE PUBLICAÇÃO

2000

RESUMO

It is well known the difficulties associated with the tradicional procedure for model identification of the Box &Jenkins model through the pattern matching of the theoretical and estimated ACF and PACF. The decision on the acceptance of the null hypothesis of zero ACF (or PACF) for a given lag is based on a strong asymptotic result, particularly for the PACF, leading, sometimes, to wrong decisions on the identified order of the models. The fuzzy logic allows one to infer system governed by incomplete or fuzzy knowledge (deductive inference) using a staighforward formulation of the problem via fuzzy mathematics. On the other hand, the neural network represent the knowledge in a implicit manner and has a great generalization capacity (inductive inference). In this thesis we built a specialist system composed of 5 fuzzy neural networks to help on the automatic identificationof the following Box &Jenkins ARMA structure AR(1), MA(1), AR(2), MA(2) and ARMA (1,1), through the Euclidian distance between the estimated output of the net and the corresponding patterns of each one of the five structures.

ASSUNTO(S)

conjuntos nebulosos fuzzy sets modelos box e jenkins fuzzy neural networks box e jenkins models redes neurais nebulosas

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