Universal approximation using probabilistic neural networks with sigmoid activation functions

In this paper we demonstrate that finite linear combinations of compositions of a fixed, univariate function and a set of affine functional can uniformly approximate any continuous function of n real variables with support in the unit hypercube; only mild conditions are imposed on the univariate function. Our results settle an open question about representability in the class of single bidden layer neural networks. In particular, we show that...