Al-Bahir Journal for Engineering and Pure Sciences


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.


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