  It is well known that Artificial Neural Networks are universal approximators.
The classical result proves that, given a continuous function on a compact set
on an n-dimensional space, then there exists a one-hidden-layer feedforward
network which approximates the function. Such result proves the existence, but
it does not provide a method for finding it. In this paper, a constructive
approach to the proof of this property is given for the case of
two-hidden-layer feedforward networks. This approach is based on an
approximation of continuous functions by simplicial maps. Once a triangulation
of the space is given, a concrete architecture and set of weights can be
obtained. The quality of the approximation depends on the refinement of the
covering of the space by simplicial complexes.
