Pointwise and uniform approximation by multivariate neural network operators of the max-product type
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Publication:2418225
DOI10.1016/j.neunet.2016.06.002zbMath1439.41009OpenAlexW2436103560WikidataQ50615208 ScholiaQ50615208MaRDI QIDQ2418225
Danilo Costarelli, Gianluca Vinti
Publication date: 3 June 2019
Published in: Neural Networks (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.neunet.2016.06.002
order of approximationuniform approximationsigmoidal functionsmax-product operatorsmultivariate neural networks operators
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Cites Work
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- Multivariate neural network operators with sigmoidal activation functions
- On the approximation by neural networks with bounded number of neurons in hidden layers
- Saturation and inverse results for the Bernstein max-product operator
- Order of approximation for sampling Kantorovich operators
- Rate of approximation for multivariate sampling Kantorovich operators on some functions spaces
- Intelligent systems. Approximation by artificial neural networks
- Constructive approximate interpolation by neural networks
- Bivariate quartic spline spaces and quasi-interpolation operators
- The approximation operators with sigmoidal functions
- A kind of improved univariate multiquadric quasi-interpolation operators
- Feedforward nets for interpolation and classification
- Constructive methods of approximation by ridge functions and radial functions
- Uniform approximation by neural networks
- Rate of convergence of some neural network operators to the unit-univariate case
- Construction techniques for highly accurate quasi-interpolation operators
- Approximation by neural networks with a bounded number of nodes at each level
- Neural network operators: constructive interpolation of multivariate functions
- On quasi-interpolation by radial basis functions with scattered centres
- Approximation with neural networks activated by ramp sigmoids
- Convergence of a family of neural network operators of the Kantorovich type
- Approximation by series of sigmoidal functions with applications to neural networks
- Necessary and sufficient condition for multistability of neural networks evolving on a closed hypercube
- Approximation by neural networks and learning theory
- Max-product neural network and quasi-interpolation operators activated by sigmoidal functions
- Degree of Approximation for Nonlinear Multivariate Sampling Kantorovich Operators on Some Functions Spaces
- On pointwise convergence of linear integral operators with homogeneous kernels
- A Natural Formulation of Quasi-Interpolation by Multivariate Splines
- Universal approximation bounds for superpositions of a sigmoidal function
- Error estimates for some quasi-interpolation operators
- On the Approximation of the Cut and Step Functions by Logistic and Gompertz Functions
- On a Variational Norm Tailored to Variable-Basis Approximation Schemes
- Approximation by superpositions of a sigmoidal function
