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|Type:||Artigo de evento|
|Title:||Second-order Training For Recurrent Neural Networks Without Teacher-forcing|
|Author:||Von Zuben Fernando J.|
de Andrade Netto Marcio L.
|Abstract:||Neural networks with external recurrences can be successfully applied to nonlinear autoregressive moving average modeling. The process of weight adjustment is presented as a nonlinear optimization problem in the N-dimensional Euclidean space, where N is the number of adjustable weights. The least-squares criterion can be effectively minimized using a version of the conjugate gradient algorithm. Expending about the same amount of computation necessary to obtain the gradient, the required second-order information is calculated exactly. A simulation example confirms the efficacy of the training process when applied to time series prediction. Contrary to the proposed method, teacher-forced learning is shown to be ill-suited for multi-step prediction.|
|Editor:||IEEE, Piscataway, NJ, United States|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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