Best approximation and inverse results for neural network operators
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Publication:6595826
DOI10.1007/S00025-024-02222-3zbMATH Open1545.41004MaRDI QIDQ6595826
Lucian Coroianu, Danilo Costarelli
Publication date: 30 August 2024
Published in: Results in Mathematics (Search for Journal in Brave)
modulus of continuityLipschitz classessigmoidal functionneural network operatorsinverse theorem of approximation
Cites Work
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- Multivariate neural network operators with sigmoidal activation functions
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- Approximation rates for neural networks with general activation functions
- Approximation by exponential sampling type neural network operators
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- Quantitative estimates for neural network operators implied by the asymptotic behaviour of the sigmoidal activation functions
- The construction and approximation of a class of neural networks operators with Ramp functions
- DENSITY RESULTS BY DEEP NEURAL NETWORK OPERATORS WITH INTEGER WEIGHTS
- Quantitative estimates involving K-functionals for neural network-type operators
- Equivalence of approximation by convolutional neural networks and fully-connected networks
- Neural Approximations for Optimal Control and Decision
- Approximation by superpositions of a sigmoidal function
- Approximation error for neural network operators by an averaged modulus of smoothness
- Fractional type multivariate neural network operators
- The multivariate Durrmeyer-sampling type operators in functional spaces
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