Approximation capability of two hidden layer feedforward neural networks with fixed weights

no code implementations • 22 Jan 2021 • Namig J. Guliyev, Vugar E. Ismailov

We algorithmically construct a two hidden layer feedforward neural network (TLFN) model with the weights fixed as the unit coordinate vectors of the $d$-dimensional Euclidean space and having $3d+2$ number of hidden neurons in total, which can approximate any continuous $d$-variable function with an arbitrary precision.

On the approximation by single hidden layer feedforward neural networks with fixed weights

no code implementations • 21 Aug 2017 • Namig J. Guliyev, Vugar E. Ismailov

Feedforward neural networks have wide applicability in various disciplines of science due to their universal approximation property.

A single hidden layer feedforward network with only one neuron in the hidden layer can approximate any univariate function

no code implementations • 31 Dec 2015 • Namig J. Guliyev, Vugar E. Ismailov

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.