4 papers
A Class of Function-Preserving Neural Network Operators with Applications to Modulated Signal Denoising
Sachin Saini
In this paper, we introduce a class of function-preserving neural network operators designed to preserve a prescribed strictly positive function. We examine the approximation behav…
Stancu-Type Generalizations of Neural Network Operators with Perturbed Sampling Nodes
Sachin Saini
In this paper, we introduce a Stancu-type generalization of multivariate neural network operators (NNOs) by incorporating two parameters that perturb the sampling nodes. The propos…
A Universal Approximation Theorem for Neural Networks with Outputs in Locally Convex Spaces
Sachin Saini
In this paper, a universal approximation theorem (UAT) for shallow neural networks whose inputs belong to a topological vector space (TVS) and whose outputs take values in a Hausdo…
Taylor-Accelerated Neural Network Interpolation Operators on Irregular Grids with Higher Order Approximation
Sachin Saini
In this paper, a new class of \emph{Taylor-accelerated neural network interpolation operators} is introduced on quasi-uniform irregular grids. These operators improve existing neur…