Neural network operators with hyperbolic tangent functions

被引：1

作者：

Baxhaku, Behar ^{[1
]}

Agrawal, Purshottam Narain ^{[2
]}

机构：

[1] Univ Prishtina, Dept Math, Prishtina, Kosovo

[2] Indian Inst Technol Roorkee, Dept Math, Roorkee 247667, India

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2023年 / 226卷

关键词：

Neural network operators; Sigmoidal functions; Tanh function; Modulus of continuity; APPROXIMATION OPERATORS; CONVERGENCE;

D O I：

10.1016/j.eswa.2023.119996

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We determine the global errors occurring as a result of applying the method of approximate approximations to a function defined on a compact interval. By the method of extending a function to a wider interval, we obtain upper bounds on the error estimates in the uniform norm for continuous and differentiable functions by using these approximation tools. We extend this study to the bivariate case by constructing the associated approximate approximation neural network operators.

引用

页数：10

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