论文标题
两层神经网络的全球通用性，具有$ k $的线性单元
Global universality of the two-layer neural network with the $k$-rectified linear unit
论文作者
论文摘要
本文涉及两层神经网络的通用性，其$ k $ retectified的线性单位激活功能具有$ k = 1,2，\ ldots $，具有适当的标准，而无需限制域的形状。这种类型的结果称为全球通用性，将先前的结果扩展到本作者的$ k = 1 $。本文介绍了$ k $ -sigmoidal函数，以应用$ k $ retectified的线性单位功能的基本结果。
This paper concerns the universality of the two-layer neural network with the $k$-rectified linear unit activation function with $k=1,2,\ldots$ with a suitable norm without any restriction on the shape of the domain. This type of result is called global universality, which extends the previous result for $k=1$ by the present authors. This paper covers $k$-sigmoidal functions as an application of the fundamental result on $k$-rectified linear unit functions.
