On the $L^p$-Convergence and Denoising Performance of Durrmeyer-Type Max-Min Neural Network Operators

Berke Şahin; İsmail Aslan

doi:10.48550/arxiv.2601.22174

On the $L^p$-Convergence and Denoising Performance of Durrmeyer-Type Max-Min Neural Network Operators

arXiv (Cornell University) 2026 Preprint Open access

Read full text

Authors: Berke Şahin, İsmail Aslan

DOI: 10.48550/arxiv.2601.22174

Cited by: 0

Abstract

In this paper, we investigate Durrmeyer-type generalizations of maximum-minimum neural network operators. The primary objective of this study is to establish the convergence of these operators in the $L^{p}$ norm for functions $f\in L^{p}([a,b],[0,1])$ with $1\leq p

Fields of Study & Subjects

Approximation Theory and Sequence Spaces Neural Networks and Applications Mathematical Inequalities and Applications

Publication details

Journal / source: arXiv (Cornell University)
Publication date: 24 January 2026
Document type: Preprint