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Approximation by network operators with logistic activation functions

Author

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  • Chen, Zhixiang
  • Cao, Feilong
  • Hu, Jinjie

Abstract

This paper aims to study the construction and multivariate approximation of a class of network operators with logistic sigmoidal functions. First, a class of even and bell-shaped function with support on R is constructed by using appropriate translation and combination of the logistic function. Then, the constructed function is employed as activation function to construct a kind of so-called Cardaliaguet–Euvrard type network operators. Finally, these network operators are used to approximate bivariate functions in C[-1,1]2, and a Jackson type theorem for the approximation errors is established.

Suggested Citation

  • Chen, Zhixiang & Cao, Feilong & Hu, Jinjie, 2015. "Approximation by network operators with logistic activation functions," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 565-571.
  • Handle: RePEc:eee:apmaco:v:256:y:2015:i:c:p:565-571
    DOI: 10.1016/j.amc.2015.01.049
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    Cited by:

    1. Romanuke Vadim, 2015. "Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem," Information Technology and Management Science, Sciendo, vol. 18(1), pages 42-48, December.

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