Neural network interpolation operators activated by smooth ramp functions
From MaRDI portal
Publication:5083442
DOI10.1142/S0219530521500123zbMath1494.41007OpenAlexW3168382414MaRDI QIDQ5083442
Publication date: 20 June 2022
Published in: Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219530521500123
interpolationmodulus of continuitysimultaneous approximationuniform approximateneural network operators
Interpolation in approximation theory (41A05) Rate of convergence, degree of approximation (41A25) Simultaneous approximation (41A28) Inverse theorems in approximation theory (41A27)
Related Items (3)
Neural network interpolation operators optimized by Lagrange polynomial ⋮ Neural network interpolation operators of multivariate functions ⋮ Rates of approximation by neural network interpolation operators
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Error estimates for the modified truncations of approximate approximation with Gaussian kernels
- Approximation by neural networks with sigmoidal functions
- Intelligent systems. Approximation by artificial neural networks
- Univariate hyperbolic tangent neural network approximation
- Multivariate hyperbolic tangent neural network approximation
- Approximation by neural networks with weights varying on a finite set of directions
- The essential order of approximation for neural networks
- The construction and approximation of feedforward neural network with hyperbolic tangent function
- The approximation operators with sigmoidal functions
- The essential order of simultaneous approximation for neural networks
- Approximation by Ridge functions and neural networks with one hidden layer
- Approximation by superposition of sigmoidal and radial basis functions
- Uniform approximation by neural networks
- Univariant approximation by superpositions of a sigmoidal function
- Rate of convergence of some neural network operators to the unit-univariate case
- Neural network operators: constructive interpolation of multivariate functions
- Multilayer feedforward networks are universal approximators
- Approximation of functions of finite variation by superpositions of a sigmoidal function.
- An approximation by neural networks with a fixed weight
- Degree of approximation by neural and translation networks with a single hidden layer
- Approximation with neural networks activated by ramp sigmoids
- Voronovskaja type theorems and high-order convergence neural network operators with sigmoidal functions
- Theory of deep convolutional neural networks: downsampling
- Interpolation by neural network operators activated by ramp functions
- Convergence of a family of neural network operators of the Kantorovich type
- Approximation by series of sigmoidal functions with applications to neural networks
- Error bounds for approximations with deep ReLU networks
- Universality of deep convolutional neural networks
- The estimation of simultaneous approximation order for neural networks
- Universal approximation bounds for superpositions of a sigmoidal function
- Neural Networks for Localized Approximation
- Deep distributed convolutional neural networks: Universality
- Quantitative estimates involving K-functionals for neural network-type operators
- On Approximation by Neural Networks with Optimized Activation Functions and Fixed Weights
- Approximation by superpositions of a sigmoidal function
- Approximation by superpositions of a sigmoidal function
This page was built for publication: Neural network interpolation operators activated by smooth ramp functions