Differentiable approximation by means of the Radon transformation and its applications to neural networks

DOI10.1016/0377-0427(94)90183-XzbMath0827.65028OpenAlexW2018829818MaRDI QIDQ1891035

Yoshifusa Ito

Publication date: 11 December 1995

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0377-0427(94)90183-x

zbMATH Keywords

neural networkderivativesinverse Radon transformdelta sequencesdifferentiable approximation

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Radon transform (44A12) Numerical differentiation (65D25)

Related Items (2)

Nonlinearity creates linear independence ⋮ Simultaneous Approximations of Polynomials and Derivatives and Their Applications to Neural Networks

Cites Work

• The Radon transform
• Multilayer feedforward networks are universal approximators
• Approximation by superpositions of a sigmoidal function
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