Large Data Analysis via Interpolation of Functions: Interpolating Polynomials vs Artificial Neural Networks

In this article we study function interpolation problem from interpolating polynomials and artificial neural networks point of view. Function interpolation plays a very important role in many areas of experimental and theoretical sciences. Usual means of function interpolation are interpolation polynomials (Lagrange, Newton, splines, Bezier, etc.). Here we show that a specific strategy of function interpolation realized by means of artificial neural networks is much efficient than, e.g., Lagrange interpolation polynomial.