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An approach towards generating surrogate models by using RBFN with a priori bias
Department of Mechanical Engineering, School of Engineering, Jönköping University Jönköping, Sweden .ORCID iD: 0000-0001-7534-0382
Department of Engineering Science, University West, Trollhättan, Sweden.ORCID iD: 0000-0001-6821-5727
2014 (English)In: Proceedings of the ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, 2014, Vol. 2B, New York, USA: ASME Press, 2014, V02BT03A024Conference paper, Published paper (Refereed)
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Abstract [en]

In this paper, an approach to generate surrogate modelsconstructed by radial basis function networks (RBFN) with a prioribias is presented. RBFN as a weighted combination of radialbasis functions only, might become singular and no interpolationis found. The standard approach to avoid this is to add a polynomialbias, where the bias is defined by imposing orthogonalityconditions between the weights of the radial basis functionsand the polynomial basis functions. Here, in the proposed a prioriapproach, the regression coefficients of the polynomial biasare simply calculated by using the normal equation without anyneed of the extra orthogonality prerequisite. In addition to thesimplicity of this approach, the method has also proven to predictthe actual functions more accurately compared to the RBFNwith a posteriori bias. Several test functions, including Rosenbrock,Branin-Hoo, Goldstein-Price functions and two mathematicalfunctions (one large scale), are used to evaluate the performanceof the proposed method by conducting a comparisonstudy and error analysis between the RBFN with a priori and aposteriori known biases. Furthermore, the aforementioned approachesare applied to an engineering design problem, that ismodeling of the material properties of a three phase sphericalgraphite iron (SGI) . The corresponding surrogate models arepresented and compared

Place, publisher, year, edition, pages
New York, USA: ASME Press, 2014. V02BT03A024
Keyword [en]
Optimization, Response Surface, Surrogate Modelling, RBF, RBFN, Approximation Function
National Category
Applied Mechanics Mechanical Engineering
Research subject
Mechanical Engineering
Identifiers
URN: urn:nbn:se:oru:diva-48247ISI: 000379987300024Scopus ID: 2-s2.0-84961312861ISBN: 978-0-7918-4632-2 (print)OAI: oai:DiVA.org:oru-48247DiVA: diva2:904515
Conference
ASME, International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE, Buffalo, NY, USA, August 17-20, 2014
Available from: 2016-02-18 Created: 2016-02-15 Last updated: 2017-10-18Bibliographically approved

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Amouzgar, KavehStrömberg, Niclas
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