A robust prediction method based on Kriging method and fuzzy c-means algorithm with application to a combine harvester-Reference-Cited by-同舟云学术

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A robust prediction method based on Kriging method and fuzzy c-means algorithm with application to a combine harvester

Published:2022-09 Issue:9 Volume:65 Page:
ISSN:1615-147X
Container-title:Structural and Multidisciplinary Optimization
language:en
Short-container-title:Struct Multidisc Optim

Author:

Shi Maolin,Liang Zhenwei,Zhang Jian,Xu Lizhang,Song Xueguan

Funder

Natural Science Foundation of Jiangsu Province

Project funded by China Postdoctoral Science Foundation

Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment

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/s00158-022-03364-0.pdf

Reference41 articles.

1. Abdallah I, Lataniotis C, Sudret B (2019) Parametric hierarchical Kriging for multi-fidelity aero-servo-elastic simulators—Application to extreme loads on wind turbines. Probab Eng Mech 55:67–77

2. Aldoshin, N., Didmanidze, O., Lylin, N., & Mosyakov, M. (2019). Work improvement of air-and-screen cleaner of combine harvester. In Engineering for Rural Development: Proceedings of 18th International Scientific Conference (Latvia University of Life Sciences and Technologies, Faculty of Engineering, Jelgava, May 22–24, 2019) (Vol. 18, p. 100).

3. An J, Owen A (2001) Quasi-regression. J Complex 17(4):588–607

4. Aute V, Saleh K, Abdelaziz O, Azarm S, Radermacher R (2013) Cross-validation based single response adaptive design of experiments for Kriging metamodeling of deterministic computer simulations. Struct Multidisc Optim 48(3):581–605

5. Ben-Ari EN, Steinberg DM (2007) Modeling data from computer experiments: an empirical comparison of Kriging with MARS and projection pursuit regression. Qual Eng 19(4):327–338

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