Radial basis functions with a priori bias as surrogate models: A comparative study

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Amouzgar, Kaveh,1980-Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Materialmekanik, Mechanics of Materials (author)

Radial basis functions with a priori bias as surrogate models : A comparative study

Article/chapterEnglish2018

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Elsevier,2018
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LIBRIS-ID:oai:DiVA.org:his-14999
https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-14999URI
https://doi.org/10.1016/j.engappai.2018.02.006DOI

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Language:English
Summary in:English

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Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

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©2018 Elsevier Ltd. All rights reserved.
Radial basis functions are augmented with a posteriori bias in order to perform robustly when used as metamodels. Recently, it has been proposed that the bias can simply be set a priori by using the normal equation, i.e., the bias becomes the corresponding regression model. In this study, we demonstrate the performance of the suggested approach (RBFpri) with four other well-known metamodeling methods; Kriging, support vector regression, neural network and multivariate adaptive regression. The performance of the five methods is investigated by a comparative study, using 19 mathematical test functions, with five different degrees of dimensionality and sampling size for each function. The performance is evaluated by root mean squared error representing the accuracy, rank error representing the suitability of metamodels when coupled with evolutionary optimization algorithms, training time representing the efficiency and variation of root mean squared error representing the robustness. Furthermore, a rigorous statistical analysis of performance metrics is performed. The results show that the proposed radial basis function with a priori bias achieved the best performance in most of the experiments in terms of all three metrics. When considering the statistical analysis results, the proposed approach again behaved the best, while Kriging was relatively as accurate and support vector regression was almost as fast as RBFpri. The proposed RBF is proven to be the most suitable method in predicting the ranking among pairs of solutions utilized in evolutionary algorithms. Finally, the comparison study is carried out on a real-world engineering optimization problem.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Maskinteknik hsv//swe
ENGINEERING AND TECHNOLOGY Mechanical Engineering hsv//eng
Kriging
Metamodeling
Multivariate adaptive regression splines
Neural networks
Radial basis function
Support vector regression
Surrogate models
Errors
Evolutionary algorithms
Functions
Heat conduction
Image segmentation
Interpolation
Mean square error
Optimization
Regression analysis
Statistical methods
Radial basis functions
Support vector regression (SVR)
Surrogate model
Radial basis function networks
Mechanics of Materials
Materialmekanik
Production and Automation Engineering
Produktion och automatiseringsteknik

Added entries (persons, corporate bodies, meetings, titles ...)

Bandaru, Sunith,1984-Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Produktion och automatiseringsteknik, Production and Automation Engineering(Swepub:his)bans (author)
Ng, Amos H. C.,1970-Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Produktion och automatiseringsteknik, Production and Automation Engineering(Swepub:his)ngam (author)
Högskolan i SkövdeInstitutionen för ingenjörsvetenskap (creator_code:org_t)

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In:Engineering applications of artificial intelligence: Elsevier71, s. 28-440952-19761873-6769

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