幂级数展开神经网络
Power Series Expansion Neural Network
摘要：
本文提出了一种基于幂级数展开的神经网络族，并与已有的神经网络进行了比较，结果表明该神经网络族具有更好的逼近精度。这组新的神经网络将幂级数展开（PSE）嵌入到神经网络结构中。然后，它可以提高表示能力，同时保持相当的计算成本，通过增加的程度，而不是增加的深度或宽度的PSE。理论逼近和数值结果都表明了这种新的神经网络的优点。
英文摘要：
In this paper, we develop a new neural network family based on power series expansion, which is proved to achieve a better approximation accuracy in comparison with existing neural networks. This new set of neural networks embeds the power series expansion (PSE) into the neural network structure. Then it can improve the representation ability while preserving comparable computational cost by increasing the degree of PSE instead of increasing the depth or width. Both theoretical approximation and numerical results show the advantages of this new neural network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
