Approximation and Shape Preserving Properties of Neural Network Operators
Abstract
Abstract In this paper, approximation and shape preserving properties for the so-called neural network (NN) operators have been faced. First, the case of the general NN operators based on sigmoidal function has been considered; moreover, theorems relating to uniform convergence, Korovkin-type results, and endpoint behavior have been also obtained. Furthermore, the special case of NN operators based on the ramp function and on the central B-splines, respectively, have been taken into account.
Fields of Study & Subjects
Approximation Theory and Sequence Spaces
Neural Networks and Applications
Fuzzy Logic and Control Systems
Publication details
- Journal / source
- Results in Mathematics
- Publisher
- Springer Science and Business Media LLC
- Volume
- 81
- Issue
- 5
- Publication date
- 15 June 2026
- Language
- EN