A feasible identification method of uncertainty responses for vehicle structures-Reference-Cited by-同舟云学术

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A feasible identification method of uncertainty responses for vehicle structures

Published:2021-09-06 Issue:6 Volume:64 Page:3861-3876
ISSN:1615-147X
Container-title:Structural and Multidisciplinary Optimization
language:en
Short-container-title:Struct Multidisc Optim

Author:

Xu Xiang,Chen Xinbo,Liu Zhe,Zhang Yong,Xu Yanan,Fang Jianguang,Gao Yunkai

Funder

the project of shanghai science and technology committee

the prospective technology project of nanchang intelligent new energy vehicle research institute

the national natural science foundation of china

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Computer Graphics and Computer-Aided Design,Computer Science Applications,Control and Systems Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s00158-021-03065-0.pdf

Reference43 articles.

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2. Awad M, Qasrawi I (2018) Enhanced RBF neural network model for time series prediction of solar cells panel depending on climate conditions (temperature and irradiance). Neural Comput Appl 30:1757–1768. https://doi.org/10.1007/s00521-016-2779-5

3. Brown D, Ling L, Kansa E, Levesley J (2005) On approximate cardinal preconditioning methods for solving PDEs with radial basis functions. Eng Anal Bound Elem 29:343–353. https://doi.org/10.1016/j.enganabound.2004.05.006

4. Cao L, Liu J, Xie L, Jiang C, Bi R (2021) Non-probabilistic polygonal convex set model for structural uncertainty quantification. Appl Math Model 89:504–518. https://doi.org/10.1016/j.apm.2020.07.025

5. Chen G, Yang D (2021) A unified analysis framework of static and dynamic structural reliabilities based on direct probability integral method. Mech Syst Signal Process 158:107783. https://doi.org/10.1016/j.ymssp.2021.107783

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