scholarly journals Universal approximation theorem for Dirichlet series

2006 ◽  
Vol 2006 ◽  
pp. 1-11 ◽  
Author(s):  
O. Demanze ◽  
A. Mouze
Keyword(s):  
Dirichlet Series ◽  
Simultaneous Approximation ◽  
Shift Operator ◽  
Approximation Theorem ◽  
Extension Theorem ◽  
Universal Approximation ◽  
Derivation Operator ◽  
Backward Shift ◽  
The Right ◽  
Density Results

The paper deals with an extension theorem by Costakis and Vlachou on simultaneous approximation for holomorphic function to the setting of Dirichlet series, which are absolutely convergent in the right half of the complex plane. The derivation operator used in the analytic case is substituted by a weighted backward shift operator in the Dirichlet case. We show the similarities and extensions in comparing both results. Several density results are proved that finally lead to the main theorem on simultaneous approximation.

scholarly journals Approximation by Jakimovski-Leviatan-Stancu-Durrmeyer type operators

Filomat ◽  
2019 ◽  
Vol 33 (6) ◽  
pp. 1517-1530 ◽  
Author(s):  
M. Mursaleen ◽  
Shagufta Rahman ◽  
Khursheed Ansari
Keyword(s):  
Upper Bound ◽  
Simultaneous Approximation ◽  
Approximation Theorem ◽  
Approximation Properties ◽  
Statistical Approximation ◽  
Durrmeyer Operators ◽  
Durrmeyer Type Operators

In the present paper, we introduce Stancu type modification of Jakimovski-Leviatan-Durrmeyer operators. First, we estimate moments of these operators. Next, we study the problem of simultaneous approximation by these operators. An upper bound for the approximation to rth derivative of a function by these operators is established. Furthermore, we obtain A-statistical approximation properties of these operators with the help of universal korovkin type statistical approximation theorem.

scholarly journals A Universal Approximation Theorem for Mixture-of-Experts Models

2016 ◽  
Vol 28 (12) ◽  
pp. 2585-2593 ◽  
Author(s):  
Hien D. Nguyen ◽  
Luke R. Lloyd-Jones ◽  
Geoffrey J. McLachlan
Keyword(s):  
Network Architecture ◽  
Approximation Theorem ◽  
Target Function ◽  
Continuous Functions ◽  
Universal Approximation ◽  
Mixture Of Experts ◽  
Neural Network Architecture ◽  
Alternative Result ◽  
Unit Hypercube ◽  
Uniformly Convergent

The mixture-of-experts (MoE) model is a popular neural network architecture for nonlinear regression and classification. The class of MoE mean functions is known to be uniformly convergent to any unknown target function, assuming that the target function is from a Sobolev space that is sufficiently differentiable and that the domain of estimation is a compact unit hypercube. We provide an alternative result, which shows that the class of MoE mean functions is dense in the class of all continuous functions over arbitrary compact domains of estimation. Our result can be viewed as a universal approximation theorem for MoE models. The theorem we present allows MoE users to be confident in applying such models for estimation when data arise from nonlinear and nondifferentiable generative processes.

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