基于Stacking集成元模型的作战体系能力图谱生成方法

doi:10.12305/j.issn.1001-506X.2022.01.20

摘要/Abstract

摘要：

作战体系能力图谱是从整体分析作战体系能力的有效方法。针对生成作战体系能力图谱时, 需要对海量实验空间进行仿真而带来时效性较差的问题, 提出一种基于Stacking集成元模型的作战体系能力图谱生成方法。该方法通过建立元模型替代仿真模型, 能够快速准确生成作战体系能力图谱。针对各类元模型适用场景有限、性能波动较大的问题, 提出采用Stacking方法对多种元模型进行集成, 提高不同应用环境下元模型的鲁棒性; 针对建立元模型时精度与效率的矛盾问题, 提出适用于体系能力图谱生成的序贯采样策略, 通过计算拟合不确定性, 选取高价值样本, 在达到相同准确性指标的同时减少采样次数。最后, 通过实验案例分析, 证明了所提方法的有效性。

关键词: 作战体系, 能力图谱, Stacking集成学习, 元模型

Abstract:

Operational system of system (SoS) capability graph is an effective method to analyze operational SoS capability as a whole. Aiming at the problem of timeliness caused by the need to simulate the massive experimental space when generating the operational SoS capability graph, a method of generating the operational SoS capability graph based on the Stacking integrated meta-model is proposed. The method generated the operational SoS capability graph quickly and accurately by building the meta-model instead of the simulation model. Aiming at the problems of limited application scenarios and fluctuating performance of various meta-model, the Stacking method is proposed to integrate multiple meta-models to improve the robustness of meta-models in different application environments. Aiming at the contradiction between accuracy and efficiency in the meta-model building, a sequential sampling strategy is proposed for the generation of operational SoS capability graph. By calculating the meta-model uncertainty, high-value samples are selected to reduce the number of sampling while achieving the same accuracy index. Finally, an experimental case analysis proves the proposed effectiveness of the method.

Key words: operational system of systems, capability graph, Stacking ensemble learning, meta-model

中图分类号:

E917
TP391.9

马骏, 杨镜宇, 邹立岩. 基于Stacking集成元模型的作战体系能力图谱生成方法[J]. 系统工程与电子技术, 2022, 44(1): 154-163.

Jun MA, Jingyu YANG, Liyan ZOU. Generation method of operational system of systems capability graph based on Stacking integrated meta-model[J]. Systems Engineering and Electronics, 2022, 44(1): 154-163.

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实验案例		PRS-model	Kriging-model	RBF-model	SVR-model	Stacking-model
目标指示	案例1	0.905 7±0.017 5	0.889 3±0.036 5	0.901 6±0.031 1	0.908 0±0.058 7	0.920 1±0.016 4
	案例2	0.539 9±0.039 1	0.763 1±0.175 7	0.889 3±0.158 4	0.834 0±0.092 5	0.931 2±0.019 8
	案例3	0.826 0±0.137 3	0.792 0±0.044 9	0.897 2±0.049 6	0.941 2±0.023 6	0.933 4±0.018 0
	案例4	0.891 2±0.059 2	0.915 0±0.007 7	0.939 9±0.023 9	0.917 8±0.025 4	0.935 2±0.022 9
目标打击	案例1	0.842 4±0.078 6	0.790 1±0.068 2	0.849 9±0.188 9	0.910 0±0.088 5	0.930 5±0.019 3
	案例2	0.505 4±0.511 2	0.685 1±0.042 2	0.761 3±0.202 8	0.919 7±0.044 9	0.927 1±0.018 7
	案例3	0.668 0±0.157 4	0.605 0±0.066 8	0.741 7±0.124 4	0.928 8±0.021 7	0.924 6±0.017 6
	案例4	0.885 3±0.051 0	0.792 0±0.044 9	0.930 8±0.047 5	0.910 8±0.032 2	0.935 3±0.022 2

案例	PRS-model	Kriging-model	RBF-model	SVR-model	Stacking-model
目标指示	0.170 8	0.073 7	0.022 5	0.046 3	0.006 8
目标打击	0.174 1	0.090 4	0.087 1	0.008 8	0.004 6
综合	0.163 4	0.100 3	0.074 7	0.032 2	0.005 4

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