Univariate Neural Network Quantitative (NNQ) Approximation by Symmetrized Operators - George A. Anastassiou; Seda Karateke; Metin Zontul (Fractal and Fractional, 2025)
This paper deals not only with pointwise and uniform convergence but also Y-valued fractional approximation results by univariate symmetrized neural network (SNN) operators on Banach space <semantics><mfenced separators="" open="(" close=")">Y,<mfenced open="∥" close="∥">.</mfenced></mfenced></semantics>. Moreover, our main motivation in this work is to compare the convergence results obtained by classical neural network (NN) operators and symmetric neural network (SNN) operators and try to convert them into numerical examples and graphs through computer programming language Python codes. As a result of this experimental study conducted under the regime of certain parameters, the convergence speed and results of SNN operators are superior to those of classical NN operators.