Convergence Analysis of Max-Min Exponential Neural Network Operators in Orlicz Space
This work addresses theoretical convergence analysis for neural network operators in approximation theory, but it appears incremental as it builds on existing exponential neural network frameworks.
The paper tackles the problem of approximating functions using max-min exponential neural network operators, extending them to Kantorovich-type operators and analyzing their convergence in Orlicz space, with results including pointwise and uniform convergence estimates using the logarithmic modulus of continuity.
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.