Constructive methods of approximation by ridge functions and radial functions

DOI10.1007/BF02144104zbMath0764.41017OpenAlexW2018270822MaRDI QIDQ1208704

E. W. Cheney, William A. Light, Yuan Xu

Publication date: 16 May 1993

Published in: Numerical Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02144104

zbMATH Keywords

ridge functionsunivariate function

Mathematics Subject Classification ID

Approximation by other special function classes (41A30)

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Techniques for the mathematical analysis of neural networks, Approximation theorems for a family of multivariate neural network operators in Orlicz-type spaces, Approximation by max-product neural network operators of Kantorovich type, Characterization of an extremal sum of ridge functions, On local and global sigma-pi neural networks. A common link, Neural network operators: constructive interpolation of multivariate functions, Meshless method with ridge basis functions, Asymptotic expansions and Voronovskaja type theorems for the multivariate neural network operators, Pointwise and uniform approximation by multivariate neural network operators of the max-product type, On the representation by linear superpositions, Convergence results for a family of Kantorovich max-product neural network operators in a multivariate setting, Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces, Convergence for a family of neural network operators in Orlicz spaces, Approximation by finite mixtures of continuous density functions that vanish at infinity, Where there's a Will there's a way -- the research of Will Light, Techniques for generating approximations via convolution kernels

Cites Work

• Approximation by superposition of sigmoidal and radial basis functions
• The fundamentality of sets of ridge functions
• Approximation by superpositions of a sigmoidal function
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