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A Neural Network Approximation Based on a Parametric Sigmoidal Function

https://doi.org/10.3390/math7030262
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/bf02551274openalex-snapshot
Approximation by superpositions of a sigmoidal function
inferredcites →https://doi.org/10.1016/0021-9045(92)90081-xopenalex-snapshot
Approximation by ridge functions and neural networks with one hidden layer
inferredcites →https://doi.org/10.1016/0893-6080(89)90003-8openalex-snapshot
On the approximate realization of continuous mappings by neural networks
inferredcites →https://doi.org/10.1016/0893-6080(90)90005-6openalex-snapshot
Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
inferredcites →https://doi.org/10.1016/j.camwa.2003.06.008openalex-snapshot
An approximation by neural networkswith a fixed weight
inferredcites →https://doi.org/10.1016/j.camwa.2009.05.001openalex-snapshot
The approximation operators with sigmoidal functions
inferredcites →https://doi.org/10.1016/j.neucom.2007.07.024openalex-snapshot
The estimate for approximation error of neural networks: A constructive approach
inferredcites →https://doi.org/10.1016/s0893-6080(98)00068-9openalex-snapshot
Constructive function-approximation by three-layer artificial neural networks
inferredcites →https://doi.org/10.1109/18.256500openalex-snapshot
Universal approximation bounds for superpositions of a sigmoidal function
inferredcites →https://doi.org/10.1109/tnn.2004.836233openalex-snapshot
Smooth Function Approximation Using Neural Networks
inferredcites →https://doi.org/10.11145/j.biomath.2013.12.301openalex-snapshot
Cell Growth Models Using Reaction Schemes: Batch Cultivation
inferredcites →https://doi.org/10.11145/j.bmc.2015.03.081openalex-snapshot
Sigmoidal Functions: Some Computational and Modelling Aspects
inferredcites →https://doi.org/10.1137/s1064827502414606openalex-snapshot
An Extended Sigmoidal Transformation Technique for Evaluating Weakly Singular Integrals without Splitting the Integration Interval
inferredcites →https://doi.org/10.1155/2015/376362openalex-snapshot
A Smoothening Method for the Piecewise Linear Interpolation
inferredcites →https://doi.org/10.4208/ata.2013.v29.n2.8openalex-snapshot
Constructive Approximation by Superposition of Sigmoidal Functions

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-53212-2_40openalex-snapshot
A Note on the Hwang-Kim’s Universal Activation Function
inferred← citeshttps://doi.org/10.1007/s00521-022-07211-7openalex-snapshot
KAF + RSigELU: a nonlinear and kernel-based activation function for deep neural networks
inferred← citeshttps://doi.org/10.1063/1.5127466openalex-snapshot
A note on the xgamma cumulative sigmoid. Some applications
inferred← citeshttps://doi.org/10.1063/5.0040120openalex-snapshot
Comments on the Epsilon and Omega cumulative distributions: “Saturation in the Hausdorff sense”
inferred← citeshttps://doi.org/10.1109/access.2024.3404613openalex-snapshot
DIRBW-Net: An Improved Inverted Residual Network Model for Underwater Image Enhancement
inferred← citeshttps://doi.org/10.1109/icaiic68212.2026.11454209openalex-snapshot
Revisiting the Expressive Power of Simple ReLU Neural Networks
inferred← citeshttps://doi.org/10.11145/bmc.2019.04.307openalex-snapshot
On a Logistic Differential Model. Some Applications
inferred← citeshttps://doi.org/10.11145/j.biomath.2020.05.033openalex-snapshot
A new class of activation functions. Some related problems and applications
inferred← citeshttps://doi.org/10.1186/s13662-023-03787-zopenalex-snapshot
Universal approximation property of a continuous neural network based on a nonlinear diffusion equation
inferred← citeshttps://doi.org/10.33640/2405-609x.2362openalex-snapshot
L_p_ Approximation by ReLU Neural Networks
inferred← citeshttps://doi.org/10.3390/a13120324openalex-snapshot
Some Notes on the Omega Distribution and the Pliant Probability Distribution Family
inferred← citeshttps://doi.org/10.3390/math7111133openalex-snapshot
Discrete Mutation Hopfield Neural Network in Propositional Satisfiability
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