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An approach towards generating surrogate models by using RBFN with a apriori bias
Högskolan i Jönköping, Tekniska Högskolan, JTH. Forskningsmiljö Produktutveckling - Simulering och optimering.ORCID-id: 0000-0001-7534-0382
University of West.
2014 (engelsk)Inngår i: Proceedings of the ASME 2014 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2014 August 17-20, 2014, Buffalo, NY, USA, American Society of Mechanical Engineers (ASME) , 2014Konferansepaper, Publicerat paper (Fagfellevurdert)
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

sted, utgiver, år, opplag, sider
American Society of Mechanical Engineers (ASME) , 2014.
Emneord [en]
Optimization, Response Surface, Surrogate Modelling, RBF, RBFN, Approximation Function
HSV kategori
Identifikatorer
URN: urn:nbn:se:hj:diva-24673OAI: oai:DiVA.org:hj-24673DiVA, id: diva2:744347
Konferanse
ASME, International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE, Buffalo, NY, August 17-20, 2014
Tilgjengelig fra: 2014-09-08 Laget: 2014-09-08 Sist oppdatert: 2018-09-13bibliografisk kontrollert
Inngår i avhandling
1. Metamodel based multi-objective optimization
Åpne denne publikasjonen i ny fane eller vindu >>Metamodel based multi-objective optimization
2015 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

As a result of the increase in accessibility of computational resources and the increase in the power of the computers during the last two decades, designers are able to create computer models to simulate the behavior of a complex products. To address global competitiveness, companies are forced to optimize their designs and products. Optimizing the design needs several runs of computationally expensive simulation models. Therefore, using metamodels as an efficient and sufficiently accurate approximate of the simulation model is necessary. Radial basis functions (RBF) is one of the several metamodeling methods that can be found in the literature.

The established approach is to add a bias to RBF in order to obtain a robust performance. The a posteriori bias is considered to be unknown at the beginning and it is defined by imposing extra orthogonality constraints. In this thesis, a new approach in constructing RBF with the bias to be set a priori by using the normal equation is proposed. The performance of the suggested approach is compared to the classic RBF with a posteriori bias. Another comprehensive comparison study by including several modeling criteria, such as problem dimension, sampling technique and size of samples is conducted. The studies demonstrate that the suggested approach with a priori bias is in general as good as the performance of RBF with a posteriori bias. Using the a priori RBF, it is clear that the global response is modeled with the bias and that the details are captured with radial basis functions.

Multi-objective optimization and the approaches used in solving such problems are briefly described in this thesis. One of the methods that proved to be efficient in solving multi-objective optimization problems (MOOP) is the strength Pareto evolutionary algorithm (SPEA2). Multi-objective optimization of a disc brake system of a heavy truck by using SPEA2 and RBF with a priori bias is performed. As a result, the possibility to reduce the weight of the system without extensive compromise in other objectives is found.

Multi-objective optimization of material model parameters of an adhesive layer with the aim of improving the results of a previous study is implemented. The result of the original study is improved and a clear insight into the nature of the problem is revealed.

sted, utgiver, år, opplag, sider
Jönköping: Jönköping University, School of Engineering, 2015. s. 25
Serie
JTH Dissertation Series ; 13
Emneord
Multi-objective optimization, strength Pareto evolutionary algorithm, SPEA2, metamodel, surrogate model, response surface, radial basis functions, RBF
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-28432 (URN)978-91-87289-14-9 (ISBN)
Presentation
2015-12-11, E1405, School of Engineering, Gjuterigatan 5, Jönköping, 14:00
Opponent
Veileder
Tilgjengelig fra: 2015-12-02 Laget: 2015-12-02 Sist oppdatert: 2018-01-10bibliografisk kontrollert

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