Abstract. We present a learning algorithm for feedforward neural networks that is based on Kolmogorov theorem concerning composition of ndimensional continuous function from one-dimensional continuous functions. A thorough analysis of the algorithm time complexity is presented together with serial and parallel implementation examples.
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