1989年由George Cybenko证明的数学定理，指出只要层数和神经元足够多，神经网络可以以任意精度逼近任何连续函数，是深度学习理论基础之一。
The universal approximation theorem was rigorously proven by mathematician Cybenko in 1989 for the Sigmoid activation function.