  According to conventional neural network theories, the feature of
single-hidden-layer feedforward neural networks(SLFNs) resorts to parameters of
the weighted connections and hidden nodes. SLFNs are universal approximators
when at least the parameters of the networks including hidden-node parameter
and output weight are exist. Unlike above neural network theories, this paper
indicates that in order to let SLFNs work as universal approximators, one may
simply calculate the hidden node parameter only and the output weight is not
needed at all. In other words, this proposed neural network architecture can be
considered as a standard SLFNs with fixing output weight equal to an unit
vector. Further more, this paper presents experiments which show that the
proposed learning method tends to extremely reduce network output error to a
very small number with only 1 hidden node. Simulation results demonstrate that
the proposed method can provide several to thousands of times faster than other
learning algorithm including BP, SVM/SVR and other ELM methods.
