Research and Improvement of Kriging-HDMR Modeling Method

被引：1
|
作者：
Meng Y. [1 ,2 ,3 ]
Shi B. [1 ,2 ,3 ]
Zhang D. [1 ,2 ,3 ]
机构：
[1] State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin
[2] Key Laboratory of Advanced Intelligent Protective Equipment Technology of Ministry of Education, Hebei University of Technology, Tianjin
[3] School of Mechanical Engineering, Hebei University of Technology, Tianjin
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2024年 / 60卷 / 05期
关键词：
design of experiment; DIRECT sampling; high dimensional model representation; industrial robot; Kriging;
D O I：
10.3901/JME.2024.05.249
中图分类号：
学科分类号：
摘要：
When dealing with high-dimensional problems in traditional representation model technology, the number of sample points required for modeling increases exponentially due to the increase in variable dimension, which will lead to a significant increase in computational cost. In order to build a representation model suitable for high-dimensional problems, based on the DIRECT optimization algorithm, the initial sample point position is improved, the initial sample set is expanded, and the Kriging modeling method and high-dimensional model representation (HDMR), which can avoid the established model from falling into local optimality, and proposes an improved Kriging-HDMR (iKriging-HDMR) modeling method. The iKriging-HDMR modeling method uses the advantages of HDMR to equate the response function of the high-dimensional problem to a series of low-dimensional function superpositions, taking advantage of the iKriging-HDMR modeling method to reduce the number of sample points required in the modeling process number. A new convergence condition is proposed to reduce the local error of the agent model to ensure that the established agent model has high accuracy. The effectiveness of the proposed method is verified by numerical examples and robot engineering applications. The results show that the proposed iKriging-HDMR modeling method can significantly reduce the number of sample points required for modeling, and has good calculation accuracy and efficiency. © 2024 Chinese Mechanical Engineering Society. All rights reserved.
引用
收藏
页码：249 / 263
页数：14
相关论文
共 32 条
  • [1] Xin WEI, WU Yizhong, CHEN Liping, DIRECT global optimization method using radial basis functions[J], Journal of Huazhong University of Science and Technology, 40, 5, pp. 6-9, (2012)
  • [2] CUI Baozhen, KONG Weina, MA Kai, Application of polynomial response surface surrogate model in the large column structure optimization[J], Journal of Machine Design, 34, 4, pp. 44-48, (2017)
  • [3] LI Tiezhu, LI Guangyao, CHEN Tao, Et al., Robustness design of occupant restraint system based on Kriging model[J], Journal of Mechanical Engineering, 46, 22, pp. 123-129, (2010)
  • [4] LI Enying, WANG Hu, LI Guangyao, Material parameter inverse technique based on support vector regression[J], Journal of Mechanical Engineering, 48, 6, pp. 90-95, (2012)
  • [5] HAN Zhonghua, Kriging surrogate model and its application to design optimization：a review of recent progress[J], Acta Aeronautica et Astronautica Sinica, 37, 11, pp. 3197-3225, (2016)
  • [6] FORRESTER A, KEANE A., Recent advances in surrogate-based optimization[J], Progress in Aerospace Sciences, 45, 1, pp. 50-79, (2009)
  • [7] PENG Lei, Li LIU, LONG Teng, Optimization strategy using dynamic radial basis function metamodel[J], Journal of Mechanical Design, 47, 7, pp. 164-170, (2011)
  • [8] LONG Teng, GUO Xiaosong, PENG Lei, Et al., Optimization strategy using dynamic radial basis function metamodel based on trust region[J], Journal of Mechanical Engineering, 50, pp. 184-190, (2014)
  • [9] CHOWDHURY R, RAO B, PRASAD A., High dimensional model representation for structural reliability analysis[J], Communications in Numerical Methods in Engineering, 25, 4, pp. 301-337, (2009)
  • [10] WU Xiaojing, PENG Xuhao, CHEN Weisheng, Et al., A developed surrogate-based optimization framework combining HDMR-based modeling technique and TLBO algorithm for high-dimensional engineering problems[J], Structural and Multidisciplinary Optimization, 60, 2, pp. 663-680, (2019)
1234