  In this work, a method of random parameters generation for randomized
learning of a single-hidden-layer feedforward neural network is proposed. The
method firstly, randomly selects the slope angles of the hidden neurons
activation functions from an interval adjusted to the target function, then
randomly rotates the activation functions, and finally distributes them across
the input space. For complex target functions the proposed method gives better
results than the approach commonly used in practice, where the random
parameters are selected from the fixed interval. This is because it introduces
the steepest fragments of the activation functions into the input hypercube,
avoiding their saturation fragments.
