Original Contribution: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function

Several researchers characterized the activation functions under which multilayer feedforwardnetworks can act as universal approximators. We show that all the characterizationsthat were reported thus far in the literature ark special cases of the following general result:a standard multilayer feedforward network can approximate any continuous functionto any degree of accuracy if and only if the network's activation functions are not polynomial.We also emphasize the important role of the threshold, asserting that without it thelast theorem doesn't hold.

Multilayer feedforward networks with non-polynomial activation functions can approximate any function

Original Contribution: Approximation of dynamical systems by continuous time recurrent neural networks

http://www.profileofpcnayak.org/neuro_fuzzy_JH04.pdf

H White

Papers

Feed forward networks are universal approximators

Mlp's are universal approximators