EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 38 / No 3 (June 2020) JNWPU, 38 3 (2020) 677-684 References
Open Access
Issue
JNWPU
Volume 38, Number 3, June 2020
Page(s) 677 - 684
DOI https://doi.org/10.1051/jnwpu/20203830677
Published online 06 August 2020
  1. Liu Jun, Song Wenping, Han Zhonghua, et al. Comparative Study of GEK(Gradient-Enhanced Kriging) and Kriging When Applied to Design Optimization[J]. Journal of Northwestern Polytechnical University, 2015, 33 (5): 819– 826 [Article][Article](in Chinese) [Google Scholar]
  2. Zhang Z, Demory B, Henner M, et al. Space Infill Study of Kriging Meta-Model for Multi-Objective Optimization of an Engine Cooling Fan[C]//Proceedings of the ASME Turbo Expo 2014, Düsseldorf, Germany, 2014 [Google Scholar]
  3. Konka A, Coit D W, Smith A E. Multi-Objective Optimizationusing Genetic Algorithms:a Tutorial[J]. Reliability Engnieering and System Safety, 2006, 91: 992– 1007 [Article] [Google Scholar]
  4. Han Zhonghua. Kriging Surrogate Model and Its Application to Design Optimization:a Review of Recent Progress[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37 (11): 3197– 3225 [Article](in Chinese) [Google Scholar]
  5. Han Zhonghua, Xu Chenzhou, Zhang Liang, et al. Efficient Aerodynamic Shape Optimization Using Variable-Fidelity Surrogate Models and Multilevel Computational Grids[J]. Chinese Journal of Aeronautics, 2020, 33 (1): 31– 47 [Article] [Google Scholar]
  6. Cottrell M, Olteanu M, Rossi F, et al. Theoretical and Applied Aspects of the Self-Organizing Maps[J]. Advances in Intelligent Systems and Computing, 2016, 428: 3– 26 [Article] [Google Scholar]
  7. Kohonen T. The Self-Organizing Map[J]. Neurocomputing, 1998, 21 (1): 1– 6 [Article] [Google Scholar]
  8. Vesanto J, Himberg J, Alhomieni E, et al. SOM Toolbox for Matlab5[R]. Report A57, Helsinki University of Technology, 2000 [Google Scholar]
  9. Yang Li, Tong Cao. Reliability Analysis of Gear Vibration Based on Dimensionality Reduction Visualization and Kriging[J]. Journal of Aerospace Power, 2016, 31 (4): 993– 999 [Article](in Chinese) [Google Scholar]
  10. Zhang Yiwen, Xiang Tao, Guo Xing, et al. Quality Prediction for Services Based on Som Neural Network[J]. Journal of Software, 2018, 29 (11): 3388– 3399 [Article](in Chinese) [Google Scholar]
  11. Li Yang, Hao Zhifeng, Xie Guangqiang, et al. Quality-Metrics Driven Multi-Dimensional Data Aggregation and Visualization[J]. CAAI Trans on Intelligent Systems, 2013, 8 (4): 299– 304 [Article](in Chinese) [Google Scholar]
  12. Guo Hong, Cen Shaoqi, Zhang Shaolin. Cylindrical and Conical Hydrostatic Sliding Bearing Design[M]. Zhengzhou: Zhengzhou University Press, 2013: 260– 266 (in Chinese) [Google Scholar]
  13. Delgado S, Gonzalo C, Martinez E, et al. Improvement of Self-Organizing Maps with Growing Capability for Goodness Evaluation of Multispectral Training Patters, Geosciences and Temote Sensing Symposium[J]. IEEE Trans on Nueral Networks, 2004, 62 (17): 564– 567 [Google Scholar]
  14. Deb K, Agrawal S, Pratap A, et al. A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-Ⅱ[C]//International Conference on Parallel Problem Solving from Nature, 2000: 849–858 [Google Scholar]
  15. Wang Dandan. Perormance Optimization Analysis of the Journal-Thrust Floating Ring Bearing with Independent Oil Supply and Software Development[D]. Zhengzhou: Zhengzhou University, 2010(in Chinese) [Google Scholar]
  16. Liu Haojie. Performance Analysis and Experiment Research of Hybrid Bearing Based on FLUENT[D]. Zhengzhou: Zhengzhou University, 2014(in Chinese) [Google Scholar]
  17. Zhong Hong, Zhang Guankun. Hydrostatic and Hydrostatic Bearing Design Handbook[M]. Beijing: Publishing House of Electronics Industry, 2007: 138– 156 (in Chinese) [Google Scholar]
  18. Zhang Zebin, Zhang Pengfei, Guo Hong, et al. Implementation of Kriging Model Based Sequential Design on the Optimization of Sliding Bearing[J]. Journal of Harbin Institute of Technology, 2019, 51 (7): 178– 183 [Article](in Chinese) [Google Scholar]
  19. Liu Jun. Efficient Surrogate-Based Optimization Method and Its Application in Aerodynamic Design[D]. Xi'an: Northwestern Polytechnical University, 2015(in Chinese) [Google Scholar]
  20. Giunta A, Wojtkiewicz S, Eldred M. Overview of Modern Design of Experiments Methods for Computational Simulations[C]//41st Aerospace Sciences Meeting and Exhibit, 2003: 649 [Google Scholar]
  21. Leary S, Bhaskar A, Keane A. Optimal Orthogonal-Array-Based Latin Hypercubes[J]. Journal of Applied Statistics, 2003, 30 (5): 585– 598 [Article] [CrossRef] [Google Scholar]
  22. Wang Gangcheng, Ma Ning, Gu Xiezhong. Fast Collaborative Multi-Objective Optimization for Hydrodynamic Based on Kriging Surrogate Model[J]. Journal of Shanghai Jiaotong, 2018, 52 (6): 666– 673 [Article](in Chinese) [Google Scholar]
  23. Li Quan. Research on Visual Analytics for Multidimensional Data and Micro-Blogging Social Network[D]. Beijing: Tsinghua University, 2012(in Chinese) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.