勒贝格可积函数
勒贝格可积函数-
用构造性的方法证明对任何定义在多维欧氏空间紧集上的勒贝格可积函数以及它的导数可以用一个单隐层的神经网络同时逼近。
It is shown in this paper by a constructive method that for any Lebesgue integrable functions defined on a compact set in a multidimensional Euclidian space , the function and its derivatives can be simultaneously approximated by a neural network with one hidden layer .