Order of Approximation for Exponential Sampling Type Neural Network Operators

被引：0
作者：
Shivam Bajpeyi
机构：
[1] Indian Institute of Technology Delhi,Department of Mathematics
来源：
Results in Mathematics | 2023年 / 78卷
关键词：
Exponential sampling; Neural network operators; Order of convergence; Mellin transform; Jackson type inequalities; 41A05; 41A35; 47A58; 94A20; 41A25;
D O I：
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学科分类号：
摘要：
In the present article, we derive certain direct approximation results for the family of exponential sampling type neural network operators. The Voronovskaja type theorem of convergence for these operators is proved. Further, the Jackson-type inequalities concerning the order of approximation for these family of operators are established by utilizing the notion of logarithmic modulus of continuity for the involved functions and their higher derivatives. In order to improve the order of convergence, we provide a constructive mechanism by considering the linear combination of these operators. At the end, we also discuss a few numerical examples based on the presented theory.
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