  Randomized methods of neural network learning suffer from a problem with the
generation of random parameters as they are difficult to set optimally to
obtain a good projection space. The standard method draws the parameters from a
fixed interval which is independent of the data scope and activation function
type. This does not lead to good results in the approximation of the strongly
nonlinear functions. In this work, a method which adjusts the random
parameters, representing the slopes and positions of the sigmoids, to the
target function features is proposed. The method randomly selects the input
space regions, places the sigmoids in these regions and then adjusts the
sigmoid slopes to the local fluctuations of the target function. This brings
very good results in the approximation of the complex target functions when
compared to the standard fixed interval method and other methods recently
proposed in the literature.
