Translations:Neural Networks/20/en

The universal approximation theorem (Cybenko 1989, Hornik 1991) states that a feedforward network with a single hidden layer containing a finite number of neurons can approximate any continuous function on a compact subset of $ \mathbb{R}^n $ to arbitrary accuracy, provided the activation function satisfies mild conditions (e.g. is non-constant, bounded, and continuous).