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Type: Artigo de evento
Title: A Recurrent Neurofuzzy Network Structure And Learning Procedure
Author: Ballini R.
Soares S.
Gomide F.
Abstract: A novel recurrent neurofuzzy network is proposed in this paper. This model is constructed from fuzzy set models of neurons. The network has a multilayer, recurrent structure whose units are modeled through triangular norms and co-norms, and weights defined within the unit interval. The learning procedure developed is based on two main paradigms: gradient search and associative reinforcement learning, respectively. That is, output layer weights are adjusted via an error gradient method whereas a reward and punishment scheme updates the hidden layer weights. The recurrent neurofuzzy network is used to develop a model of a nonlinear process. Numerical results show that the neurofuzzy network proposed here provides an accurate process model after a short period of learning time.
Citation: Ieee International Conference On Fuzzy Systems. , v. 3, n. , p. 1408 - 1411, 2001.
Rights: fechado
Date Issue: 2001
Appears in Collections:Unicamp - Artigos e Outros Documentos

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