[1] 中国质量协会.中国制造业企业质量管理蓝皮书[M]. 北京: 人民出版社, 2021.China association for quality. The blue book on the quality management of China’s manufacturing enterprises[M]. Beijing: People’s Publishing House, 2021. [2] 工业和信息化部科技司. 全面强化质量管理数字化能力 加快推动制造业高质量发展[N]. 中国电子报, 2022-01-14(006).Ministry of industry and information technology, science and technology department. Comprehensively strengthening the digitization capability in quality management to accelerate the high-quality development for manufacturing industries[N]. China Electronics News, 2022-01-14(006). [3] 封晓斌, 汤易兵, 吴增源,等. 基于SRFML-Lift 的流程制造产品质量状态监测[J]. 中国管理科学, 2021, 29(12): 227-236.Feng Xiaobin, Tang Yibin, Wu Zengyuan, et al. Process manufacturing product quality status monitoring based on SRFML-Lift[J]. Chinese Journal of Management Science, 2021, 29(12): 227-236. [4] 赵丽, 程铁信, 莫莹,等. 基于C5.0改进算法的焊接工艺参数选择决策树数据挖掘模型及其应用[J]. 中国管理科学, 2016, 24(S1): 177-182.Zhao Li, Chen Tiexin, Mo Ying, et al. The decision tree data mining model for welding parameters selection based on C5.0 improved algorithm and its application[J]. Chinese Journal of Management Science, 2016, 24(S1): 177-182. [5] 邵景峰, 贺兴时, 王进富,等. 基于数据的纺纱质量异常行为预警四步法[J]. 中国管理科学, 2015, 23(S1): 275-284.Shao Jingfeng, He Xingshi, Wang Jinfu, et al. A four-step method for abnormal behavior warning of spinning quality based on data[J]. Chinese Journal of Management Science, 2015, 23(S1): 275-284. [6] 徐兰, 方志耕, 刘思峰. 基于神经网络集成的质量预测模型[J]. 系统管理学报, 2013, 22(6): 823-827. Xu Lan, Fang Zhigeng, Liu Sifeng. Quality prediction model based on neural network ensemble[J]. Journal of Systems & Management, 2013, 22(6): 823-827. [7] 马义中, 汪建均, 欧阳林寒,等. 复杂产品的质量控制理论与方法[M]. 北京:科学出版社,2021.Ma Yizhong, Wang Jianjun, Ouyang Linhan, et al. Quality control theory and methods for complex products[M]. Beijing: Science Press, 2021. [8] 林成龙, 马义中, 肖甜丽,等. 基于PLS方法和Kriging模型的多目标有效全局优化方法及应用[J]. 系统工程理论与实践, 2021, 41(7): 1855-1867.Lin Chenglong, Ma Yizhong, Xiao Tianli, et al. Multi-objective efficient global optimization method and its application based on PLS method and Kriging model[J]. Systems Engineering-Theory & Practice, 2021, 41(7): 1855-1867. [9] 韩忠华. Kriging模型及代理优化算法研究进展[J]. 航空学报, 2016, 37(11): 3197-3225.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. [10] 何志昆, 刘光斌, 赵曦晶,等. 高斯过程回归方法综述[J]. 控制与决策, 2013, 28(8): 1121-1129.He Zhikun, Liu Guangbin, Zhao Xijing, et al. Overview of Gaussian process regression[J]. Control and Decision, 2013, 28(8): 1121-1129. [11] Chen Xi, Zhou Qiang. Sequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitation[J]. European Journal of Operational Research, 2017, 262(2): 575-585. [12] Feng Zebiao, Wang Jianjun, Ma Yizhong, et al. Robust parameter design based on Gaussian process with model uncertainty[J]. International Journal of Production Research, 2021, 59(9): 2772-2788. [13] Wang Songhao, Ng S H, Haskell W B. A multilevel simulation optimization approach for quantile functions[J]. Informs Journal on Computing, 2021, 34(1): 569-585. [14] 冯泽彪, 汪建均, 马义中. 基于多变量高斯过程模型的贝叶斯建模与稳健参数设计[J]. 系统工程理论与实践, 2020, 40(3): 703-713.Feng Zebiao, Wang Jianjun, Ma Yizhong. Bayesian modeling and robust parameter design based on multivariate Gaussian process model[J]. Systems Engineering-Theory & Practice, 2020, 40(3): 703-713. [15] 张鹏, 张树有, 伊国栋,等. 面向零件轻量化设计的自适应动态 Kriging 模型及应用[J]. 计算机集成制造系统, 2019, 25(3): 726-735.Zhang Peng, Zhang Shuyou, Yi Guodong, et al. Adaptively dynamic Kriging model for parts lightweight design and its application[J]. Computer Integrated Manufacturing Systems, 2019, 25(3): 726-735. [16] Han Mei, Ouyang Linhan. Robust functional response-based metamodel optimization considering both location and dispersion effects for aeronautical airfoil designs[J]. Structural and Multidisciplinary Optimization, 2021,64(3):1545-1565. [17] Feng Kaixuan, Lu Zhenzhou, Wang Lu. A novel dual-stage adaptive Kriging method for profust reliability analysis[J]. Journal of Computational Physics, 2020, 419: 109701. [18] Mehmani A, Chowdhury S, Meinrenken C, et al. Concurrent surrogate model selection (COSMOS): optimizing model type, kernel function, and hyper-parameters[J]. Structural and Multidisciplinary Optimization, 2018, 57(3): 1093-1114. [19] Ouyang Linhan, Wan Liangqi, Park C, et al. Ensemble RBF modeling technique for quality design[J]. Journal of Management Science and Engineering, 2019, 4(2): 105-118. [20] Ginsbourger D, Helbert C, Carraro L. Discrete mixtures of kernels for Kriging-based optimization[J]. Quality and Reliability Engineering International, 2008, 24(6): 681-691. [21] Palar P S, Shimoyama K. Efficient global optimization with ensemble and selection of kernel functions for engineering design[J]. Structural and Multidisciplinary Optimization, 2019, 59(1): 93-116. [22] 肖甜丽, 马义中, 林成龙. 面向质量设计的Kriging组合建模技术[J]. 计算机集成制造系统, 2021, 27(7): 2023-2034.Xiao Tianli, Ma Yizhong, Lin ChengLong. Ensemble Kriging modeling technique for quality design[J]. Computer Integrated Manufacturing Systems, 2021, 27(7): 2023-2034. [23] Gnen M, Alpaydn E. Multiple kernel learning algorithms[J]. The Journal of Machine Learning Research, 2011, 12: 2211-2268. [24] Jin S S. Accelerating Gaussian process surrogate modeling using compositional kernel learning and multi-stage sampling framework[J]. Applied Soft Computing, 2021, 104: 106909. [25] Palar P S, Zuhal L R, Shimoyama K. Gaussian Process surrogate model with composite kernel learning for engineering design[J]. AIAA Journal, 2020, 58(1):1-17. [26] Kronberger G, Kommenda M. Evolution of covariance functions for gaussian process regression using genetic programming[C]// Proceedings of the International Conference on Computer Aided Systems Theory, Las Palmas de Gran Canaria, Spain, February 10-15, 2013. [27] Peng C Y, Wu C F J. On the choice of nugget in kriging modeling for deterministic computer experiments[J]. Journal of Computational and Graphical Statistics, 2014, 23(1): 151-168. [28] Forrester A I J, Keane A J, Bressloff N W. Design and analysis of “Noisy” computer experiments[J]. AIAA journal, 2006, 44(10): 2331-2339. [29] Lee W J, Verzakov S, Duin R P W. Kernel combination versus classifier combination[C]//Proceedings of International Workshop on Multiple Classifier Systems, Berlin, Heidelberg, May 23-25, Springer, 2007: 22-31. [30] Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods[M]. Cambridge: Cambridge University Press, 2000. [31] 汪洪桥, 孙富春, 蔡艳宁,等. 多核学习方法[J]. 自动化学报, 2010, 36(8): 1037-1050.Wang Hongqiao, Sun Fuchun, Cai Yanning, et al. On multiple kernel learning methods[J]. Acta Automatica Sinica, 2010, 36(8): 1037-1050. [32] Zhou Xiaojian, Ma Yizhong, Tu Yiliu, et al. Ensemble of surrogates for dual response surface modeling in robust parameter design[J]. Quality and Reliability Engineering International, 2013, 29(2): 173-197.
|