Time-Delayed recurrent neural network models preserve information through time and are more powerful than the static feedforward networks, especially in dynamic problems. At present, recurrent networks are mostly formulated as nonlinear autoregression models [4] when applied to time series prediction problem. In this paper, we use a novel approach to interpret the recurrent networks. We build the linkage between Fourier analysis and recurrent networks. The major advantage of our method is that it provides a means to initialize the weights. This initialization significantly shortens the training time.
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