Approximation by neural networks with sigmoidal functions
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Publication:381075
DOI10.1007/s10114-013-1730-2zbMath1311.41015OpenAlexW2108930884MaRDI QIDQ381075
Publication date: 15 November 2013
Published in: Acta Mathematica Sinica. English Series (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10114-013-1730-2
Neural networks for/in biological studies, artificial life and related topics (92B20) Rate of convergence, degree of approximation (41A25) Approximation by arbitrary nonlinear expressions; widths and entropy (41A46)
Related Items (8)
On Approximation by Neural Networks with Optimized Activation Functions and Fixed Weights ⋮ Neural network interpolation operators activated by smooth ramp functions ⋮ Modified neural network operators and their convergence properties with summability methods ⋮ Computational homogenization of nonlinear elastic materials using neural networks ⋮ Neural network interpolation operators optimized by Lagrange polynomial ⋮ Neural network interpolation operators of multivariate functions ⋮ The universal approximation capabilities of double \(2\pi\)-periodic approximate identity neural networks ⋮ Rates of approximation by neural network interpolation operators
Cites Work
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- Multilayer feedforward networks are universal approximators
- An approximation by neural networks with a fixed weight
- Degree of approximation by neural and translation networks with a single hidden layer
- Universal approximation bounds for superpositions of a sigmoidal function
- Approximation by superpositions of a sigmoidal function
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