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Correlation Discriminator of Signals in the Class of Fast Neural Networks

The paper presents methods for constructing topology and parametric training of fast neural networks (FNN) for tasks of signal correlation measurements. A mathematical model of the FNN is described. The connection of fast algorithms with self-similar structures is noted. A method of multiplicative representation of arbitrary discrete functions is shown. Algorithms for constructing the FNN topology and training for implementing a correlation discriminator of signals are proposed.