Abstract
This paper introduces a new neural architecture for prediction: Double Recurrent Radial Basis Function network (R 2 RBF). The proposed R 2 RFR is excited by the recurrence of output looped neurons on the input layer. An unsupervised learning algorithm is proposed to determine the parameters of the Radial Basis Function (RBF) nodes. An application of the R 2 RBF network on the values prediction of the time series confirmed that the proposed architecture minimizes the prediction error.
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