Kolmogorov learning for feedforward networks

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