Change search
ReferencesLink to record
Permanent link

Direct link
An approach towards generating surrogate models by using RBFN with a apriori bias
Jönköping University, School of Engineering, JTH. Research area Product Development - Simulation and Optimization.ORCID iD: 0000-0001-7534-0382
University of West.
2014 (English)In: 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, 2014Conference paper (Refereed)
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
Keyword [en]
Optimization, Response Surface, Surrogate Modelling, RBF, RBFN, Approximation Function
National Category
Applied Mechanics
URN: urn:nbn:se:hj:diva-24673OAI: diva2:744347
ASME, International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE, Buffalo, NY, August 17-20, 2014
Available from: 2014-09-08 Created: 2014-09-08 Last updated: 2015-12-02Bibliographically approved
In thesis
1. Metamodel based multi-objective optimization
Open this publication in new window or tab >>Metamodel based multi-objective optimization
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Jönköping: Jönköping University, School of Engineering, 2015. 25 p.
JTH Dissertation Series, 13
Multi-objective optimization, strength Pareto evolutionary algorithm, SPEA2, metamodel, surrogate model, response surface, radial basis functions, RBF
National Category
Computer Engineering Mechanical Engineering
urn:nbn:se:hj:diva-28432 (URN)978-91-87289-14-9 (ISBN)
2015-12-11, E1405, School of Engineering, Gjuterigatan 5, Jönköping, 14:00
Available from: 2015-12-02 Created: 2015-12-02 Last updated: 2015-12-02Bibliographically approved

Open Access in DiVA

fulltext(1883 kB)32 downloads
File information
File name FULLTEXT01.pdfFile size 1883 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Amouzgar, Kaveh
By organisation
JTH. Research area Product Development - Simulation and Optimization
Applied Mechanics

Search outside of DiVA

GoogleGoogle Scholar
Total: 32 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 367 hits
ReferencesLink to record
Permanent link

Direct link