Single hidden layer feed-forward neural network(SLFN) is one of the most widely used models for intelligent
modeling. But the model faces that for small sample sets, the traditional learning algorithm may train a model to fall into the
over-fitting sate. In particular, when the dataset contains a large amount of noise, the trained model has weak robustness and
is very sensitive to noise. In order to overcome this shortcoming, a robust learning algorithm of SLFN is derived for small and
noisy datasets. Due to the introduction of   -insensitive learning measure and the structural risk term, the proposed algorithm
can effectively overcome the shortcoming of the traditional learning algorithm. The experimental results on simulated and
real-world datasets also confirm the above advantages.