Scholarlink

In this chapter we study the multivariate quantitative smooth approximation under differentiation of functions. The approximators here are multivariate neural network operators activated by Richard’s curve, a parametrized form of logistic sigmoid function. All domains used here are infinite. The multivariate neural network operators are of quasi-interpolation type: the basic ones, the Kantorovich type ones, and of the quadrature type. We give pointwise and uniform multivariate approximations with rates. We finish with applications.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multivariate Parametrized Logistic Neural Network Approximation over Infinite Domains

  • 章节

  • 发表日期：

    2026年

查看全文
  • George A. Anastassiou

摘要

In this chapter we study the multivariate quantitative smooth approximation under differentiation of functions. The approximators here are multivariate neural network operators activated by Richard’s curve, a parametrized form of logistic sigmoid function. All domains used here are infinite. The multivariate neural network operators are of quasi-interpolation type: the basic ones, the Kantorovich type ones, and of the quadrature type. We give pointwise and uniform multivariate approximations with rates. We finish with applications.