Approximation by Exponential Type Neural Network Operators

11/13/2019
by   S. Bajpeyi, et al.
0

In the present article, we introduce and study the behaviour of the new family of exponential type neural network operators activated by the sigmoidal functions. We establish the point-wise and uniform approximation theorems for these NN (Neural Network) operators in C[a; b]: Further, the quantitative estimates of order of approximation for the proposed NN operators in C(N)[a; b] are established in terms of the modulus of continuity. We also analyze the behaviour of the family of exponential type quasi-interpolation operators in C(R+): Finally, we discuss the multivariate extension of these NN operators and some examples of the sigmoidal functions.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/15/2019

Direct and inverse results for Kantorovich type exponential sampling series

In this article, we analyze the behaviour of the new family of Kantorovi...
research
12/15/2020

Approximation by quasi-interpolation operators and Smolyak's algorithm

We study approximation of multivariate periodic functions from Besov and...
research
05/21/2018

A General Family of Robust Stochastic Operators for Reinforcement Learning

We consider a new family of operators for reinforcement learning with th...
research
06/13/2022

Superiority of GNN over NN in generalizing bandlimited functions

We constructively show, via rigorous mathematical arguments, that GNN ar...
research
07/04/2023

A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture

Numerical modeling of localizations is a challenging task due to the evo...
research
12/17/2020

Annihilation Operators for Exponential Spaces in Subdivision

We investigate properties of differential and difference operators annih...
research
02/11/2022

Algebraic function based Banach space valued ordinary and fractional neural network approximations

Here we research the univariate quantitative approximation, ordinary and...

Please sign up or login with your details

Forgot password? Click here to reset