Approximation by exponential sampling type neural network operators

被引：0
作者：
Shivam Bajpeyi
A. Sathish Kumar
机构：
[1] Visvesvaraya National Institute of Technology,Department of Mathematics
来源：
Analysis and Mathematical Physics | 2021年 / 11卷
关键词：
Exponential sampling series; Neural network operators; Order of convergence; Logarithmic modulus of continuity; 41A35; 47A58; 94A20; 41A25;
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摘要：
In the present article, we introduce and analyse the approximation properties of the new family of exponential sampling type neural network operators activated by the sigmoidal functions. We derive the pointwise and uniform convergence theorem and study the order of approximation for these family of operators. Further, we establish the quantitative estimate for the order of convergence in terms of modulus of continuity and also discuss the convergence of exponential sampling type quasi interpolation operators. At the end, we provide a few examples of the sigmoidal functions satisfying the presented theory.
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