Prediction of time series using architecture based on neuro-fuzzy systems. / Predição não-linear de séries temporais usando sistemas de arquitetura neuro-fuzzy.

AUTOR(ES)
DATA DE PUBLICAÇÃO

2006

RESUMO

This master dissertation has as main objetive applies systems of neuro-fuzzy architecture for functions prediction in serie times. The architecture carried out is the Adaptive Neuro-Fuzzy Inference System (ANFIS). This architecture is a kind of Fuzzy Inference Systems (FIS) implemen- tation under a paradigm of arti¯cial neural networks. Making use of technology of arti¯cial neural networks, the ANFIS has the capacity of learning with environ- ment data that inserted on. As the same, the ANFIS had been implemented to be a FIS. Then it can process simbolic variables. So, an ANFIS can be described like a hibrid system. All over the chapters are showed some concepts and fundaments of Fuzzy theory, arti¯cial neural networks and hidrid systems. The purpose of the tests the ANFIS, it were been made from a logistic function and a Mackey-Glass function. This tests were against with an estimation function made by MLP net. At the end of the work are some discussions, analyses and conclusions that allows futures possibilites of applications and extensions of this work.

ASSUNTO(S)

time series analyse análise de séries temporais fuzzy neural networks sistemas híbridos lógica fuzzy hibrid systems redes neurais

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