1 |
周成宁, 张培培, 张冕, 等. 一种Kriging模型和改进子集模拟的多响应系统可靠性分析方法研究[J]. 机械科学与技术, 2020, 39 (2): 309- 314.
|
|
ZHOU C N , ZHANG P P , ZHANG M , et al. Analyzing structural reliability of multi-response system based on Kriging model and gene-ralized subset simulation[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39 (2): 309- 314.
|
2 |
刘佳, 杨克巍, 姜江, 等. 基于多保真度代理模型的装备系统参数估计方法[J]. 系统工程与电子技术, 2021, 43 (1): 130- 137.
doi: 10.3969/j.issn.1001-506X.2021.01.16
|
|
LIU J , YANG K W , JIANG J , et al. Parameter estimation method of equipment system based on multi-fidelity surrogate model[J]. Systems Engineering and Electronics, 2021, 43 (1): 130- 137.
doi: 10.3969/j.issn.1001-506X.2021.01.16
|
3 |
张路路, 陈思雅, 金光. 航天器抗辐射等效试验评估建模[J]. 系统工程与电子技术, 2021, 43 (9): 2673- 2677.
doi: 10.12305/j.issn.1001-506X.2021.09.38
|
|
ZHANG L L , CHEN S Y , JIN G . Evaluation modeling of spacecraft radiation resistance equivalent test[J]. Systems Engineering and Electronics, 2021, 43 (9): 2673- 2677.
doi: 10.12305/j.issn.1001-506X.2021.09.38
|
4 |
CHEN S S , ZHEN J , YANG S X , et al. Nonhierarchical multi-model fusion using spatial random processes[J]. International Journal for Numerical Methods in Engineering, 2016, 106 (7): 503- 526.
doi: 10.1002/nme.5123
|
5 |
YANTO , APRIYONO A , SANTOSO P B , et al. Landslide susceptible areas identification using IDW and ordinary Kriging interpolation techniques from hard soil depth at middle western central Java, Indonesia[J]. Natural Hazards (Dordrecht), 2022, 110 (2): 1405- 1416.
doi: 10.1007/s11069-021-04982-5
|
6 |
QIN F Y , GUO C P , LIU D J , et al. A comparison of ordinary Kriging, regression Kriging and REML-EBLUP for mapping soil organic matter on a regional scale[J]. Arabian Journal of Geosciences, 2022, 15 (12): 1120.
doi: 10.1007/s12517-022-10008-6
|
7 |
MUKESH R , SOMA P , KARTHIKEYAN V , et al. Ordinary Kriging-and coKriging-based surrogate model for ionospheric TEC prediction using NavIC/GPS data[J]. Acta Geophysica, 2020, 68 (5): 1529- 1547.
doi: 10.1007/s11600-020-00473-6
|
8 |
MUKESH R V , SOMA P , KARTHIKEYAN V , et al. Prediction of ionospheric vertical total electron content from GPS data using ordinary kriging-based surrogate model[J]. Astrophysics & Space Science, 2019, 364 (15): 1- 12.
|
9 |
PARDO L E , DOWD P A . The second-order stationary universal Kriging model revisited[J]. Mathematical Geology, 1998, 30 (4): 347- 378.
doi: 10.1023/A:1021740123100
|
10 |
SALAMON S J , HANSEN H J , ABBOTT D . Universal Kriging prediction of line-of-sight microwave fading[J]. IEEE Access, 2020, 8, 74743- 74758.
doi: 10.1109/ACCESS.2020.2987618
|
11 |
METHA B , TRENTI M , CHU T , et al. A geostatistical ana-lysis of multiscale metallicity variations in galaxies-Ⅱ. predicting the metallicities of H ii and diffuse ionized gas regions via universal Kriging[J]. Monthly Notices of the Royal Astronomical Society, 2022, 514 (3): 4465- 4488.
doi: 10.1093/mnras/stac1484
|
12 |
JOSEPH V R , HUNG Y , SUDJIANTO A . Blind Kriging: a new method for developing metamodels[J]. Journal of Mechanical Design, 2008, 130 (3): 31102- 31109.
doi: 10.1115/1.2829873
|
13 |
HUNG Y . Penalized blind Kriging in computer experiments[J]. Statistica Sinica, 2011, 21 (3): 1171- 1190.
doi: 10.5705/ss.2009.226
|
14 |
李涵. 计算机试验下的Kriging模型变量选择[D]. 青岛: 青岛大学, 2021.
|
|
LI H. Comparison of model selection for Kriging model in computer experiments[D]. Qingdao: Qingdao University, 2021.
|
15 |
FENG Z H , LI M , WANG X , et al. Step-by-step penalized blind Kriging methods for surrogate modeling[J]. Mathematical Problems in Engineering, 2022, 2022 (1): 1- 8.
|
16 |
ZHANG Y C , TAO S , CHEN W , et al. A latent variable approach to Gaussian process modeling with qualitative and quantitative factor[J]. Technometrics, 2020, 62 (3): 291- 302.
doi: 10.1080/00401706.2019.1638834
|
17 |
DENG X , LIN C D , LIU K W , et al. Additive Gaussian process for computer models with qualitative and quantitative factors[J]. Technometrics, 2017, 59 (3): 283- 292.
doi: 10.1080/00401706.2016.1211554
|
18 |
QIAN P Z G , WU H , WU C F J . Gaussian process models for computer experiments with qualitative and quantitative factor[J]. Technometrics, 2008, 50 (3): 383- 396.
doi: 10.1198/004017008000000262
|
19 |
ZHOU Q , QIAN P Z G , ZHOU S . A simple approach to emulation for computer models with qualitative and quantitative factors[J]. Technometrics, 2011, 53 (3): 266- 273.
doi: 10.1198/TECH.2011.10025
|
20 |
金光. 数据分析与建模方法[M]. 北京: 国防工业出版社, 2013.
|
|
JIN G . Data analysis and statistical modeling[M]. Beijing: National Defense Industry Press, 2013.
|
21 |
ZHANG Y , YAO W , YE S Y , et al. A regularization method for constructing trend function in Kriging model[J]. Structural and Multidisciplinary Optimization, 2019, 59 (4): 1221- 1239.
doi: 10.1007/s00158-018-2127-8
|
22 |
IVO C , TOM D , PIET D . OoDACE toolbox: a flexible object-oriented kriging implementation[J]. Journal of Machine Learning Research, 2014, 15, 3183- 3186.
|
23 |
武雅兰. 含有定量和定性因子计算机试验的Kriging模型[D]. 天津: 南开大学, 2012.
|
|
WU Y L. Kriging model with quantitative and qualitative factors in computer experiments[D]. Tianjin: Nankai University, 2012.
|
24 |
TIBSHIRANI R . Regression shrinkage and selection via the LASSO[J]. Journal of the Royal Statistical Society. Series B: Methodological, 1996, 58 (1): 267- 288.
doi: 10.1111/j.2517-6161.1996.tb02080.x
|
25 |
NG C T , LEE W , LEE Y . In defense of LASSO[J]. Communications in Statistics-Theory and Methods, 2022, 51 (9): 3018- 3042.
doi: 10.1080/03610926.2020.1788080
|
26 |
ZHANG Y , YAO W , CHEN X Q , et al. A penalized blind likelihood Kriging method for surrogate modeling[J]. Structural and Multidisciplinary Optimization, 2020, 61 (2): 457- 474.
doi: 10.1007/s00158-019-02368-7
|
27 |
FENG K X , LU Z Z , LING C Y , et al. An efficient computational method for estimating failure credibility by combining genetic algorithm and active learning Kriging[J]. Structural and Multidisciplinary Optimization, 2020, 62 (2): 771- 785.
doi: 10.1007/s00158-020-02534-2
|
28 |
QIAN P Z G , WU C F J . Sliced space-filling designs[J]. Biometrika, 2009, 96 (4): 945- 956.
doi: 10.1093/biomet/asp044
|
29 |
BA S , MYERS W R , BRENNEMAN W A . Optimal sliced Latin hypercube designs[J]. Technometrics, 2015, 57 (4): 479- 487.
doi: 10.1080/00401706.2014.957867
|
30 |
HUANG H , LIN D K J , LIU M , et al. Computer experiments with both qualitative and quantitative variables[J]. Technometrics, 2016, 58 (4): 495- 507.
doi: 10.1080/00401706.2015.1094416
|
31 |
FANG K F K , MA C M C , WINKER P W P . Centered L/sub 2/-discrepancy of random sampling and Latin hypercube design, and construction of uniform designs[J]. Mathematics of Computation, 2002, 71 (237): 275- 296.
|