A single hidden layer feedforward network with only one neuron in the hidden layer can approximate any univariate function
A single hidden layer feedforward network with only one neuron
in the hidden layer can approximate any univariate function
Namig J. Guliyev and Vugar E. Ismailov
Abstract. The possibility of approximating a continuous function on a compact subset of the real line by a feedforward single hidden layer neural network with a sigmoidal activation function has been studied in many papers. Such networks can approximate an arbitrary continuous function provided that an unlimited number of neurons in a hidden layer is permitted. In this paper, we consider constructive approximation on any finite interval of ℝ by neural networks with only one neuron in the hidden layer. We construct algorithmically a smooth, sigmoidal, almost monotone activation function σ providing approximation to an arbitrary continuous function within any degree of accuracy.