  A random vector functional link network (RVFL) is widely used as a universal
approximator for classification and regression problems. The big advantage of
RVFL is fast training without backpropagation. This is because the weights and
biases of hidden nodes are selected randomly and stay untrained. Recently,
alternative architectures with randomized learning are developed which differ
from RVFL in that they have no direct links and a bias term in the output
layer. In this study, we investigate the effect of direct links and output node
bias on the regression performance of RVFL. For generating random parameters of
hidden nodes we use the classical method and two new methods recently proposed
in the literature. We test the RVFL performance on several function
approximation problems with target functions of different nature: nonlinear,
nonlinear with strong fluctuations, nonlinear with linear component and linear.
Surprisingly, we found that the direct links and output node bias do not play
an important role in improving RVFL accuracy for typical nonlinear regression
problems.
