An approximation by neural networks with a fixed weight

DOI10.1016/j.camwa.2003.06.008zbMath1065.41047OpenAlexW1970705963MaRDI QIDQ1767906

Bum Il Hong, Nahmwoo Hahm

Publication date: 8 March 2005

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2003.06.008

zbMATH Keywords

convolutionneural networksigmoidal function

Mathematics Subject Classification ID

Approximation by arbitrary nonlinear expressions; widths and entropy (41A46)

Related Items (14)

On Approximation by Neural Networks with Optimized Activation Functions and Fixed Weights ⋮ Neural network interpolation operators activated by smooth ramp functions ⋮ Approximation by neural networks with sigmoidal functions ⋮ On the approximation by single hidden layer feedforward neural networks with fixed weights ⋮ Extreme learning machine collocation for the numerical solution of elliptic PDEs with sharp gradients ⋮ Multivariate sigmoidal neural network approximation ⋮ The approximation operators with sigmoidal functions ⋮ Approximation by exponential sampling type neural network operators ⋮ Unnamed Item ⋮ Quantitative approximation by perturbed Kantorovich-Choquet neural network operators ⋮ Rates of approximation by neural network interpolation operators ⋮ The construction and approximation of ReLU neural network operators ⋮ Approximation by perturbed neural network operators ⋮ Approximations by multivariate perturbed neural network operators

Cites Work

• Unnamed Item
• Approximation by Ridge functions and neural networks with one hidden layer
• Extension of localised approximation by neural networks
• Universal approximation bounds for superpositions of a sigmoidal function
• Approximation by superpositions of a sigmoidal function

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