Function approximation using a partition of the input space

Feedforward neural networks can uniformly approximate continuous functions. It is shown that a simple geometric proof of this theorem, proposed originally for networks of Heaviside units, can be extended to networks of units using a smooth output function. In order to do this, a recent result on the approximation of polynomials by fixed size networks is improved