Neural Network Operator-Based Fractal Approximation: Smoothness Preservation and Convergence Analysis
TL;DR
This work uses key approximation theory tools, such as the modulus of continuity and interpolation operators, to develop convergence results and uniform approximation error bounds, and to validate the theoretical results obtained, numerical experiments with graphical analysis using Python are provided.
Abstract
This paper introduces the construction of fractal interpolation functions (FIFs), whose graphs are the attractors of an iterated function system (IFS). Integrating concepts from approximation theory, $\alpha$-fractal functions are constructed, employing shallow neural network operators. Based on the same methodology, we developed fractal interpolation functions using only discrete function values, unlike traditional methods that require each value of the target function. In order to preserve the smoothness of the target function, a method for constructing such FIFs is introduced, employing four-layered neural network operators, i.e., whenever $f \in C^{r}[a,b]$, the corresponding FIF $f^{\alpha} \in C^{r}[a,b]$. This work uses key approximation theory tools, such as the modulus of continuity and interpolation operators, to develop convergence results and uniform approximation error bounds. To validate the theoretical results obtained, numerical experiments with graphical analysis using Python is provided.
