A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function
From MaRDI portal
Publication:5380543
DOI10.1162/NECO_a_00849zbMath1474.68153arXiv1601.00013OpenAlexW3099056690WikidataQ50647886 ScholiaQ50647886MaRDI QIDQ5380543
Namig J. Guliyev, Vugar E. Ismailov
Publication date: 5 June 2019
Published in: Neural Computation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1601.00013
Rate of convergence, degree of approximation (41A25) Networks and circuits as models of computation; circuit complexity (68Q06)
Related Items (8)
Deep sparse autoencoder for feature extraction and diagnosis of locomotive adhesion status ⋮ Stretchy binary classification ⋮ On the approximation by single hidden layer feedforward neural networks with fixed weights ⋮ An artificial neural network as a troubled-cell indicator ⋮ Computing the Approximation Error for Neural Networks with Weights Varying on Fixed Directions ⋮ Limitations of shallow nets approximation ⋮ Approximation rates for neural networks with encodable weights in smoothness spaces ⋮ On sharpness of error bounds for univariate approximation by single hidden layer feedforward neural networks
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- On the approximation by neural networks with bounded number of neurons in hidden layers
- Approximation by neural networks with weights varying on a finite set of directions
- Der Wert einiger Konstanten in der Theorie der Approximation mit Bernstein-Polynomen
- Approximation by Ridge functions and neural networks with one hidden layer
- Approximation by superposition of sigmoidal and radial basis functions
- Lower bounds for approximation by MLP neural networks
- Multilayer feedforward networks are universal approximators
- Recounting the Rationals
- Stern's Diatomic Sequence 0,1,1,2,1,3,2,3,1,4,…
- Extension of localised approximation by neural networks
- Universal approximation bounds for superpositions of a sigmoidal function
- Constructive Approximation by Superposition of Sigmoidal Functions
- Approximation by superpositions of a sigmoidal function
This page was built for publication: A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function