Response Surface Method for Reliability Analysis Based on Iteratively-Reweighted-Least-Square Extreme Learning Machines

被引：3

作者：

Ou, Yanjun ^{[1
]}

Wu, Yeting ^{[1
]}

Cheng, Jun ^{[2
]}

Chen, Yangyang ^{[3
]}

Zhao, Wei ^{[1
]}

机构：

[1] Jinan Univ, Sch Mech & Construction Engn, MOE Key Lab Disaster Forecast & Control Engn, Guangzhou 510632, Peoples R China

[2] China Construction First Grp Fifth Construction Co, Beijing 100024, Peoples R China

[3] Guangzhou Univ, Earthquake Engn Res & Test Ctr, Guangzhou 510405, Peoples R China

来源：

ELECTRONICS | 2023年 / 12卷 / 07期

基金：

中国国家自然科学基金;

关键词：

iteratively reweight; least square method; extreme learning machines; reliability analysis; Monte Carlo simulation; STRUCTURAL RELIABILITY; MONTE-CARLO; VECTOR MACHINE;

D O I：

10.3390/electronics12071741

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A response surface method for reliability analysis based on iteratively-reweighted-least-square extreme learning machines (IRLS-ELM) is explored in this paper, in which, highly nonlinear implicit performance functions of structures are approximated by the IRLS-ELM. Monte Carlo simulation is then carried out on the approximate IRLS-ELM for structural reliability analysis. Some numerical examples are given to illustrate the proposed method. The effects of parameters involved in the IRLS-ELM on accuracy in reliability analysis are respectively discussed. The results exhibit that a proper number of samples and neurons in hidden layer nodes, an appropriate regularization parameter, and the number of iterations for reweighting are of important assurance to obtain reasonable precision in estimating structural failure probability.

引用

页数：15

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