Enhanced RBF neural network metamodelling approach assisted by sliced splitting-based K-fold cross-validation and its application for the stiffened cylindrical shells

Zhixiang Wang; Yongjun Lei; Huiru CuiHeyang MiaoDapeng ZhangZeping WuGuanri Liu

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Enhanced RBF neural network metamodelling approach assisted by sliced splitting-based K-fold cross-validation and its application for the stiffened cylindrical shells

机译：Enhanced RBF neural network metamodelling approach assisted by sliced splitting-based K-fold cross-validation and its application for the stiffened cylindrical shells

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摘要

To build an enhanced radial basis function neural network (RBFNN) metamodel with improved generalization capabilities, this paper presents a novel sliced splitting-based K-fold cross-validation (SSKCV) method. Cross-validation is a promising method to construct the RBFNN metamodel but suffers the high variance and loss of information of observed sample points. To overcome the intrinsic deficiency, a sliced splitting strategy is proposed to allocate the observed sample points into K mutually exclusive and collectively exhaustive folds as evenly as possible. Further, the novel average expected prediction error (AEPE), analogous to a bias-variance tradeoff, is introduced as the loss function in SSKCV, which is more capable to evaluate the generalization error of the RBFNN metamodel. Finally, the optimal parameters in the RBFNN metamodels are determined by the SSKCV method, which enhances the metamodelling efficiency and precision. Compared with other variants of SSKCV and the state-of-art blind Kriging, the benefits of the SSKCV method in achieving excellent generalization performance are both validated in the high dimensional numerical benchmarks and the stiffened cylindrical shells.

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来源
《Aerospace science and technology》 |2022年第5期|107534.1-107534.16|共16页
作者
Zhixiang Wang; Yongjun Lei; Huiru CuiHeyang MiaoDapeng ZhangZeping WuGuanri Liu;
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作者单位

College of Aerospace Science and Engineering National University of Defense Technology, Changsha, Hunan 410073, PR China, Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions, Changsha, Hunan 410073, PR China;

College of Defense Engineering, Army Engineering University of PLA, Nanjing Jiangsu 210007, PR China;

Rocket Force University of Engineering, Xi'an, Shanxi 710025, PR ChinaCollege of Aerospace Science and Engineering National University of Defense Technology, Changsha, Hunan 410073, PR ChinaBeijing Institute of Aerospace Systems Engineering, Beijing 100076, PR China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类
关键词
RBFNN; Width parameters; Sliced splitting strategy; K-fold cross-validation; SSKCV; Stiffened cylindrical shells;

Enhanced RBF neural network metamodelling approach assisted by sliced splitting-based K-fold cross-validation and its application for the stiffened cylindrical shells

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