Convergence Analysis of Max-Min Exponential Neural Network Operators in Orlicz Space

• URL: http://arxiv.org/abs/2508.10248v1
• Date: Thu, 14 Aug 2025 00:30:56 GMT
• Title: Convergence Analysis of Max-Min Exponential Neural Network Operators in Orlicz Space
• Authors: Satyaranjan Pradhan, Madan Mohan Soren,
• Abstract summary: We propose a Max Min approach for approximating functions using exponential neural network operators.<n>We study both pointwise and uniform convergence for univariate functions.<n>We provide some graphical representations to illustrate the approximation error of the function through suitable kernel and sigmoidal activation functions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this current work, we propose a Max Min approach for approximating functions using exponential neural network operators. We extend this framework to develop the Max Min Kantorovich-type exponential neural network operators and investigate their approximation properties. We study both pointwise and uniform convergence for univariate functions. To analyze the order of convergence, we use the logarithmic modulus of continuity and estimate the corresponding rate of convergence. Furthermore, we examine the convergence behavior of the Max Min Kantorovich type exponential neural network operators within the Orlicz space setting. We provide some graphical representations to illustrate the approximation error of the function through suitable kernel and sigmoidal activation functions.

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