Neural network approximation techniques have emerged as a formidable approach in computational mathematics and machine learning, providing robust tools for approximating complex functions. By ...

In this paper, we obtain the degree of approximation of a function f in Lp(1 ≤ p ≤ ∞) norm under general conditions of the pointwise and uniform convergence of wavelet expansions associated with the ...

This paper develops a new scheme for improving an approximation method of a probability density function, which is inspired by the idea in the Hilbert space projection theorem. Moreover, we apply ...

The present paper deals with the approximation properties for exponential functions of general Durrmeyer type operators having the weights of Szász basis functions. Here we give explicit expressions ...

Figure | Artistic depiction of a diffractive optical processor for massively parallel and universal approximation of nonlinear functions. Input variables are encoded into the phase of a light wave, ...