Supplemental experimental design method of qualitative-quantitative hybrid factor based on Gaussian process model

被引:0
|
作者
Zhang L. [1 ]
Pan Z. [1 ]
Liu T. [1 ]
Jin G. [1 ]
机构
[1] College of Systems Engineering, National University of Defense Technology, Changsha
关键词
best neighborhood; exploration-exploitation; qualitative-quantitative hybrid factor; supplemental experiment design;
D O I
10.12305/j.issn.1001-506X.2023.07.18
中图分类号
学科分类号
摘要
To solve the problem that qualitative and quantitative factors exist simultaneously in naval air defense missile experimental design, a supplementary experiment design method based on the hybrid of qualitative and quantitative factors is proposed. Firstly, a qualitative-quantitative factor fusion evaluation model is constructed, and in order to solve the problem that it is hard for the qualitative and quantitative factor correlation functions to fuse in the model above, a hypersphere decomposition method is used to quantify the quantitative factor correlation function. Then, based on the prediction response model, the prediction variance exploration items are constructed. By funther calculating the prediction error of sample points, the exploration item is corrected to improve the development ability of supplementary experiment design. The best neighborhood is obtained by calculating the cross polyhedron ratio, and then the supplementary experiment points are determined in the best neighborhood by the genetic algorithm. Finally, the effectiveness of the proposed method is verified by numerical examples and case analysis. © 2023 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码:2078 / 2085
页数:7
相关论文
共 20 条
  • [1] YOU Y, JIN G, PAN Z Q, Et al., An iterated local coordinate-exchange algorithm for constructing experimental design for multi-dimensional constrained spaces, Journal of Systems Engineering and Electronics, 32, 5, pp. 1212-1220, (2021)
  • [2] ZHANG Z T, ZHANG L., The combat application of queuing theory model in formation ship to air missile air defense opera tions, Journal of Physics: Conference Series, 1570, (2020)
  • [3] FAN P F, LIU J Q, OUYANG Z H., Miss distance algorithm of terminal ship-to-air missile based on vector operation, Proc. of the IEEE Chinese Guidance, Navigation and Control Conference, (2014)
  • [4] XU Q F, CAI C, JIANG C X, Et al., Quantile regression for large-scale application, SIAM Journal on Scientific Computing, 53, 1, pp. 26-42, (2019)
  • [5] MA P, MAHONEY M W, YU B., A statistical perspective on algorithmic leveraging, Journal of Machine Learning Research, 16, 1, pp. 961-991, (2015)
  • [6] JIN R C, CHEN W, SUDJIANTO A., An efficient algorithm for constructing optimal design of computer experiment, Journal of Statistical Planning and Inference, 134, 1, pp. 268-287, (2005)
  • [7] QIAN P Z G., Nested latin hypercube design, Biometrika, 96, 4, pp. 957-970, (2009)
  • [8] XIONG S, QIAN P Z G, WU C J F., Sequential design and ana-lysis of high-accuracy and low-accuracy computer codes, Technometrics, 55, 1, pp. 37-46, (2013)
  • [9] CROMBECQ K, GORISSEN D, TOMMASI L D, Et al., A novel sequential design strategy for global surrogate modeling, Proc. of the 41st Winter Simulation Conference, pp. 731-742, (2009)
  • [10] CROMBECQ K, LAERMANS E, DHAENE T., Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling, European Journal of Operational Research, 214, 3, pp. 683-696, (2011)