Results 211 to 220 of about 65,893 (254)

Multivariate Hyperbolic Tangent Neural Network Quantitative Approximation

2011
Here we give the multivariate quantitative approximation of real and complex valued continuous multivariate functions on a box or ℝ N , N eℕ, by the multivariate quasi-interpolation hyperbolic tangent neural network operators. This approximation is obtained by establishing multidimensional Jackson type inequalities involving the multivariate modulus of
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Multivariate Approximation With Rates by Perturbed Kantorovich-Shilkret Neural Network Operators

Sarajevo Journal of Mathematics, 2022
This paper deals with the determination of the rate of convergence to the unit of Perturbed Kantorovich-Shilkret multivariate normalized neural network operators of one hidden layer. These are given through the multivariate modulus of continuity of the engaged multivariate function or its high order partial derivatives and that appears in the ...
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Multivariate Error Function Based Neural Network Operators Approximation

2015
Here we present multivariate quantitative approximations of real and complex valued continuous multivariate functions on a box or \(\mathbb {R}^{N},\) \(N\in \mathbb {N}\), by the multivariate quasi-interpolation, Baskakov type and quadrature type neural network operators.
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Multivariate Fuzzy-Random Quasi-interpolation Neural Networks Approximation

2015
In this chapter we study the rate of multivariate pointwise and uniform convergence in the q-mean to the Fuzzy-Random unit operator of multivariate Fuzzy-Random Quasi-Interpolation neural network operators.
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Neural network modeling of vector multivariable functions in ill-posed approximation problems

Journal of Computer and Systems Sciences International, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kruglov, I. A., Mishulina, O. A.
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The simultaneous approximation order of multivariate function by neural networks

International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), 2012
In this document, we first give the order of simultaneous approximation of multivariate functions defined in simplex by multivariate Bernstein polynomials. Second, we prove that multivariate polynomials defined in simplex can be simultaneously approximated arbitrarily by a sigmoidal neural networks.
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Multivariate Fuzzy-Random Error Function Relied Neural Network Approximations

2015
In this chapter we deal with the rate of multivariate pointwise and uniform convergence in the q-mean to the Fuzzy-Random unit operator multivariate Fuzzy-Random Quasi-Interpolation error function based neural network operators.
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Multivariate Quantitative Approximation by Perturbed Kantorovich–Shilkret Neural Network Operators

2018
This chapter deals with the determination of the rate of convergence to the unit of Perturbed Kantorovich–Shilkret multivariate normalized neural network operators of one hidden layer. These are given through the multivariate modulus of continuity of the engaged multivariate function or its high order partial derivatives and that appears in the ...
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High Order Multivariate Fuzzy Approximation Using Quasi-interpolation Neural Networks

2015
Here are considered in terms of multivariate fuzzy high approximation to the multivariate unit sequences of multivariate fuzzy quasi-interpolation neural network operators.
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Higher Order Multivariate Fuzzy Approximation Using Basic Neural Network Operators

2015
Here are studied in terms of multivariate fuzzy high approximation to the multivariate unit basic sequences of multivariate fuzzy neural network operators.
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