Best Neural Network Approximation by using Bernstein Polynomials with GRNN Learning Application
Journal Title: Al-Bahir - Year 2022, Vol 1, Issue 1
Abstract
Bernstein polynomials are one of the first and main tools for function approximation. On the other hand, neural networks have many useful applications in approximation and other fields as well. In this paper, we study how we benefit from properties of Bernstein polynomials to define a new version of neural networks, that can be fit approximating functions in terms of modulus of continuity. Numerically, we use neural networks to approximate some types of continuous functions. For that purpose, we use GRNN algorithm to approximate functions uniformly by using Matlab, giving some examples that confirm good rate approximation.
Authors and Affiliations
Hawraa Abbas Almurieb, Anwar Anwer Hamody
Keywords
Related Articles
- EP ID EP744518
- DOI -
- Views 43
- Downloads 0
Evaluation of Geo-SPEBH algorithm based on Bandwidth for Big Data retrieval in Cloud Computing
The fast increase in volume and speed of information created by mobile devices, along with the availability of webbased applications, has considerably contributed to the massive collection of data. Approximate Nearest N...
The Performance of Arima and Arfima in Modelling the Exchange Rate of Nigeria Currency to Other Currencies
Retraction of “Fabrication and Characterization of Wood Fiber Reinforced Polymer Composites”
Retraction of “Fabrication and Characterization of Wood Fiber Reinforced Polymer Composites”
Weakly Pseudo Primary 2-Absorbing Submodules
Study of Fusion Reactions of some Light projectiles on Medium Targets