Multivariate neural network interpolation operators

In this paper, we introduce and study fractional neural network interpolation operators activated by a sigmoidal function belonging to the extended class of multivariate sigmoidal functions. We examine the rates of approximation by the operators in L-p- spaces using the modulus of continuity. Moreover, we give some special examples with graphics for the extended class of multivariate sigmoidal functions, and present some illustrative examples to demonstrate the interpolation quality of the operators based on various activation functions. Finally, as an application, we present an efficient image processing algorithm by the proposed neural network interpolation operators for both general and medical gray-level images.(C) 2022 Elsevier B.V. All rights reserved.