  We introduce a new procedure for training of artificial neural networks by
using the approximation of an objective function by arithmetic mean of an
ensemble of selected randomly generated neural networks, and apply this
procedure to the classification (or pattern recognition) problem. This approach
differs from the standard one based on the optimization theory. In particular,
any neural network from the mentioned ensemble may not be an approximation of
the objective function.
