Approximation by superposition of sigmoidal and radial basis functions

1992/09/01 by H.N Mhaskar, H. N. Mhaskar, Charles A. Micchelli +1 · 9 citations
Computer Science · Mathematics · #Digital Filter Design and Implementation #Mathematical Approximation and Integration #Numerical Methods and Algorithms

paper · doi:10.1016/0196-8858(92)90016-p

openalex publication_date 1992/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26

Citations

• Multilayer feedforward networks are universal approximators

Cited by

• One model to solve them all: 2BSDE families via neural operators
• Distributionally robust approximation property of neural networks
• Approximation by Exponential Type Neural Network Operators
• Variational Physics-Informed Neural Networks For Solving Partial Differential Equations
• Quantum optical shallow networks
• Deep nets for local manifold learning
• Function approximation with zonal function networks with activation\n functions analogous to the rectified linear unit functions
• Reducing Parameter Space for Neural Network Training
• Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space