Given a multilayer perceptron (MLP), there are functions that can be approximated up to any degree of accuracy by the MLP without having to increase the number of the hidden nodes. Those functions belong to the closure F of the set F of the maps realizable by the MLP. In the paper, we give a list of maps with this property. In particular, it is proven that rationale belongs to F for networks with arctangent activation function and exponential belongs to F for networks with sigmoid activation function. Moreover, for a restricted class of MLPs, we prove that the list is complete and give an analytic definition of F
展开▼
机译：给定多层的Perceptron（MLP），有功能可以通过MLP近似到任何程度的精度，而无需增加隐藏节点的数量。这些功能属于MLP可实现的地图的组F的封闭F.在论文中，我们提供了具有此属性的地图列表。特别是，证明基本原理属于具有难以激活函数的网络的F f f f f for网络，对于具有SIGMOID激活功能的网络属于F.此外，对于受限制的MLP类，我们证明了该列表已完成并提供F的分析定义
展开▼
