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Real-Time Thermomechanical Modeling of PV Cell Fabrication via a POD-Trained RBF Interpolation Network

Computer Modeling in Engineering & Sciences 2020年第3期122卷 757-777页

作者： Arka Das Anthony Khoury Eduardo Divo Victor Huayamave AndresCeballos Ron Eaglin Alain Kassab Adam Payne Vijay Yelundur Hubert Seigneur Embry-Riddle Aeronautical University Department of Mechanical Engineering1 Aerospace Blvd.Daytona BeachFL 32114USA. Central Technological Corporation LongwoodFL 32779USA. University of Central Florida Department of Mechanical and Aerospace Engineering4000 Central Florida Blvd.OrlandoFL 32816USA. Suniva Inc. 5765 Peachtree Industrial Blvd.NorcrossGA 30092USA. Florida Solar Energy Center University of Central FloridaCocoa BeachFL 32922USA.

This paper presents a numerical reduced order model framework to simulate the physics of the thermomechanical processes that occur during c-Si photovoltaic(PV)cell fabrication.A response surface based on a radial basis function(RBF)interpolation network trained by a Proper Orthogonal Decomposition(POD)of the solution fields is developed for fast and accurate approximations of thermal loading conditions on PV cells during the fabrication *** outcome is a stand-alone computational tool that provides,in real time,the quantitative and qualitative thermomechanical response as a function of user-controlled input parameters for fabrication processes with the precision of 3D finite element analysis(FEA).This tool provides an efficient and effective avenue for design and optimization as well as for failure prediction of PV cells.

关键词： Silicon wafers photovoltaic cell contact firing proper orthogonal decomposition radial basis functions.

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Investigating the Synthesis of RBF Networks

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Journal of Systems Engineering and Electronics 1996年第3期7卷 25-29页

作者： V. David Sanchez A.(German Aerospace Research Establishment, DLR OberpfaffenhofenInstitute for Robottes and System DynamicsD-82230 Wessling, Germany) German Aerospace Research Establishment DLR Oberpfaffenhofen Institute for Robotics and System Dynamics D-82230 Wessling Germany

The approximation capability of RBF networks is investigated using a test function and a fixed finite number of training data. The test function used allows to confirm the recently introducedconcept of second derivative dependent placement of RBF centers. Different Gaussian RBF networksare trained varying the width and the number of centers (number of hidden units). The dependenceof the approximation error on these network parameters is studied experimentally.

关键词： Approximation error Function approximation Neural network synthesis Number of hidden units radial basis functions.

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Constructive Approximation by Superposition of Sigmoidal functions

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Analysis in Theory and Applications 2013年第2期29卷 169-196页

作者： Danilo Costarelli Renato Spigler Dipartimento di Matematica Universita` "Roma Tre" 1 Largo S. Leonardo Murialdo 00146 Rome Italy

In this paper, a constructive theory is developed for approximating func- tions of one or more variables by superposition of sigmoidal functions. This is done in the uniform norm as well as in the L^p norm. Results for the simultaneous approx- imation, with the same order of accuracy, of a function and its derivatives （whenever these exist）, are obtained. The relation with neural networks and radial basis func- tions approximations is discussed. Numerical examples are given for the purpose of illustration.

关键词： Sigmoidal functions multivariate approximation L^p approximation neural net-works radial basis functions.

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