  Neural networks with random hidden nodes have gained increasing interest from
researchers and practical applications. This is due to their unique features
such as very fast training and universal approximation property. In these
networks the weights and biases of hidden nodes determining the nonlinear
feature mapping are set randomly and are not learned. Appropriate selection of
the intervals from which weights and biases are selected is extremely
important. This topic has not yet been sufficiently explored in the literature.
In this work a method of generating random weights and biases is proposed. This
method generates the parameters of the hidden nodes in such a way that
nonlinear fragments of the activation functions are located in the input space
regions with data and can be used to construct the surface approximating a
nonlinear target function. The weights and biases are dependent on the input
data range and activation function type. The proposed methods allows us to
control the generalization degree of the model. These all lead to improvement
in approximation performance of the network. Several experiments show very
promising results.
