  In this paper, we develop a new neural network family based on power series
expansion, which is proved to achieve a better approximation accuracy comparing
to existing neural networks. This new set of neural networks can improve the
expressive power while preserving comparable computational cost by increasing
the degree of the network instead of increasing the depth or width. Numerical
results have shown the advantage of this new neural network.
