Approximation by neural network operators of convolution type activated by deformed and parametrized half hyperbolic tangent function

2025/10/07 by Asiye Arif, Arif, Asiye, T. Yurdakadim +1
Computer Science · #41A17 #41A25 #41A35 #47A58 #FOS: Mathematics #Neural Networks and Applications #Numerical Analysis (math.NA)

paper · pdf · doi:10.48550/arxiv.2510.05980

openalex publication_date 2025/10/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Here, we introduce three kinds of neural network operators of convolution type which are activated by q-deformed and \beta-parametrized half hyperbolic tangent function. We obtain quantitative convergence results to the identity operator with the use of modulus of continuity. Global smoothness preservation of our operators are also presented and the iterated versions of them are taken into the consideration.

Citations

• Approximation results on neural network operators of convolution type

Related

• Approximation results on neural network operators of convolution type
• Approximation by Exponential Type Neural Network Operators
• Approximation by Neural Network operators in L^p spaces associated with an arbitrary measure
• Approximation Results for Gradient Descent trained Neural Networks
• Univariate error function based neural network approximation