On the (1+1/2) layer neural networks as universal approximators

Paper 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 (1+1/2) layer neural network using Hcaviside function as univariate functions. it presents an explicit formula for function approximation implementable as a three-layer feedforward neural network instead of a four-layer neural networks. 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.