On the (1+1/2) layer neural networks as universal approximators
Ion Ciucă, Andrew Ware · 2002
Deals with the approximation of continuous functions by feedforward neural networks. After presenting one of the main results of Ito, the paper tries to get a universal approximator implementable as a (1+1/2) layer neural network using Heaviside functions as univariate functions. The paper presents an explicit formula for function approximation implementable as a three-layer feedforward neural network instead of a four-layer neural network. These three-layer feedforward neural networks have the same number of neurons in the hidden layer as the equivalent four-layer neural networks have in the second hidden layer.