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|Type:||Artigo de evento|
|Title:||A Recurrent Neurofuzzy Network Structure And Learning Procedure|
|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.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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