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We propose in this paper the K-means-Greedy Algorithm (KGA) model to automate the process of finding the optimal value of the number of neurons in the hidden layer. The premise is that a backpropagation (BP) network which has this optimal number of neurons in its hidden layer would be able to produce accurate predictions of unknown values of a time series that it is trained with. We show that the...
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