Multi-composite general neural network approximation over finite dimensional Banach spaces
Abstract
The functions under approximation here have as a domain a finite dimensional Banach space with dimension N∈N and are with values in RN. Exploiting some topological properties of the above we are able to perform multi-composite general Neural Network multivariate approximation to the above functions. The treatment is quantitative. We produce multivariate multi-composite general Jackson type inequalities involving the modulus of continuity of the function under approximation. The established convergences are pointwise and uniform. Our technique is expected to lead to accelerated speeds of convergence.
Fields of Study & Subjects
Approximation Theory and Sequence Spaces
Neural Networks and Applications
Mathematical Approximation and Integration
Publication details
- Journal / source
- Transactions on Computational Modeling and Intelligent Systems
- Volume
- 2
- Pages
- 10051–10051
- Publication date
- 12 January 2026
- Language
- EN