无人自主系统能力边界参数自适应判别方法

doi:10.16182/j.issn1004731x.joss.23-0775

摘要/Abstract

摘要：

为有效应对仿真测试面临的维度灾难问题，降低传统全参数空间遍历中所需的仿真次数，需要获取针对性的仿真数据以准确反映实验数据建模特征，以较少的仿真次数获得信息量丰富且代表原始数据特征的样本。提出一种面向无人自主系统能力边界参数自适应判别的数字化仿真测试模型，采用多权重结构的佳点集进行初始构建，结合自适应核函数边界点判别算法，通过高斯过程回归对模型进行迭代优化，自适应地判别无人自主系统的能力边界。实验结果表明：该方法能够降低建模所需数据量，提高自适应参数边界判别的效率，为提升智能无人系统试验的效率提供了高效途径。

关键词: 无人自主系统, 边界参数自适应判别, 高斯过程回归模型, 自适应核函数, 佳点集

Abstract:

To effectively cope with the dimension curse in simulation testing and reduce the number of simulations times needed in the traditional full-space parameter traversal, it is necessary to obtain specific simulation data to accurately reflect the modeling characteristics of the test data to obtain the informative and representative samples of the original data with a smaller number of simulations. A digital simulation test model for adaptive recognition;/of capability boundary parameters for UAS is proposed. The model is initially constructed with a good point set with a multi-weight structure; In combination with an adaptive kernel function boundary point recognition, the model is iteratively optimized by Gaussian process regression, so as to adaptively detect the capability boundary of UAS. The experimental results show that the method can reduce the amount of data required for modeling and improve the efficiency of adaptive parameter boundary recognition, which provides an approach to enhance the efficiency of intelligent UAS testing.

Key words: unmanned autonomous system, adaptive recognition of boundary parameters, gaussian process regression model, adaptive kernel function, good points set, simulation testing

中图分类号:

TP391.9

李锦文,王鹏,潘优美等 . 无人自主系统能力边界参数自适应判别方法[J]. 系统仿真学报, 2024, 36(10): 2359-2370.

Li Jinwen,Wang Peng,Pan Youmei,et al . Adaptive Recognition Method of Capability Boundary Parameters for Unmanned Autonomous Systems[J]. Journal of System Simulation, 2024, 36(10): 2359-2370.

图/表 8

图1

图2

图3

表1

图4

图5

图6

表2

参考文献 48

1	Patil K. Drone Warfare[J]. Journal of Defence Studies, 2022, 16(4): 243-251.
2	Li Chunnu. Artificial Intelligence Technology in UAV Equipment[C]//2021 IEEE/ACIS 20th International Fall Conference on Computer and Information Science (ICIS Fall). Piscataway: IEEE, 2021: 299-302.
3	Liang Mingzhou, Su Xin, Liu Xiaofeng, et al. Intelligent Ocean Convergence Platform Based on IoT Empowered with Edge Computing[J]. Journal of Internet Technology, 2020, 21(1): 235-244.
4	Loic Le Gratiet, Cannamela Claire. Cokriging-based Sequential Design Strategies Using Fast Cross-validation Techniques for Multi-fidelity Computer Codes[J]. Technometrics, 2015, 57(3): 418-427.
5	Kucherenko S, Giamalakis D, Shah N, et al. Computationally Efficient Identification of Probabilistic Design Spaces Through Application of Metamodeling and Adaptive Sampling[J]. Computers & Chemical Engineering, 2020, 132: 106608.
6	Serani A, Pellegrini R, Wackers J, et al. Adaptive Multi-fidelity Sampling for CFD-based Optimisation via Radial Basis Function Metamodels[J]. International Journal of Computational Fluid Dynamics, 2019, 33(6/7): 237-255.
7	Liu Haitao, Xu Shengli, Wang Xiaofang, et al. Optimal Weighted Pointwise Ensemble of Radial Basis Functions with Different Basis Functions[J]. AIAA Journal, 2016, 54(10): 3117-3133.
8	Diez Matteo, Volpi S, Serani Andrea, et al. Simulation-Based Design Optimization by Sequential Multi-criterion Adaptive Sampling and Dynamic Radial Basis Functions[M]//Minisci E, Vasile M, Jacques Periaux, et al. Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Cham: Springer International Publishing, 2019: 213-228.
9	Jiang Ping, Shu Leshi, Zhou Qi, et al. A Novel Sequential Exploration-exploitation Sampling Strategy for Global Metamodeling[J]. IFAC-PapersOnLine, 2015, 48(28): 532-537.
10	郭述臻, 昂海松, 蔡红明. 一种自适应抽样的代理模型构建及其在复材结构优化中的应用[J]. 复合材料学报, 2018, 35(8): 2084-2094.
	Guo Shuzhen, Haisong Ang, Cai Hongming. Construction of an Adaptive Sampling Surrogate Model and Application in Composite Material Structure Optimization[J]. Acta Materiae Compositae Sinica, 2018, 35(8): 2084-2094.
11	Steiner M, Bourinet J M, Lahmer T. An Adaptive Sampling Method for Global Sensitivity Analysis Based on Least-squares Support Vector Regression[J]. Reliability Engineering & System Safety, 2019, 183: 323-340.
12	Pan Guang, Ye Pengcheng, Wang Peng, et al. A Sequential Optimization Sampling Method for Metamodels with Radial Basis Functions[J]. The Scientific World Journal, 2014, 2014: 192862.
13	Böttcher Maria, Fuchs Alexander, Leichsenring Ferenc, et al. ELSA: An Efficient, Adaptive Ensemble Learning-based Sampling Approach[J]. Advances in Engineering Software, 2021, 154: 102974.
14	Eason J, Cremaschi S. Adaptive Sequential Sampling for Surrogate Model Generation with Artificial Neural Networks[J]. Computers & Chemical Engineering, 2014, 68: 220-232.
15	Wen Zhixun, Pei Haiqing, Liu Hai, et al. A Sequential Kriging Reliability Analysis Method with Characteristics of Adaptive Sampling Regions and Parallelizability[J]. Reliability Engineering & System Safety, 2016, 153: 170-179.
16	谢雨珩, 李智, 杨明磊, 等. 基于自适应采样算法的芳烃异构化代理模型[J]. 化工学报, 2020, 71(2): 688-697.
	Xie Yuhang, Li Zhi, Yang Minglei, et al. Surrogate Model of Aromatic Isomerization Process Based on Adaptive Sampling Algorithm[J]. CIESC Journal, 2020, 71(2): 688-697.
17	Shahsavani D, Grimvall A. An Adaptive Design and Interpolation Technique for Extracting Highly Nonlinear Response Surfaces from Deterministic Models[J]. Reliability Engineering & System Safety, 2009, 94(7): 1173-1182.
18	Liu Haitao, Xu Shengli, Ma Ying, et al. An Adaptive Bayesian Sequential Sampling Approach for Global Metamodeling[J]. Journal of Mechanical Design, 2016, 138(1): 011404.
19	J.van der Herten, Couckuyt I, Deschrijver D, et al. A Fuzzy Hybrid Sequential Design Strategy for Global Surrogate Modeling of High-dimensional Computer Experiments[J]. SIAM Journal on Scientific Computing, 2015, 37(2): A1020-A1039.
20	Jala Marjorie, Levy-Leduc Céline, Moulines Éric, et al. Sequential Design of Computer Experiments for the Assessment of Fetus Exposure to Electromagnetic Fields[J]. Technometrics, 2016, 58(1): 30-42.
21	Ajdari Ali, Mahlooji Hashem. An Adaptive Exploration-Exploitation Algorithm for Constructing Metamodels in Random Simulation Using a Novel Sequential Experimental Design[J]. Communications in Statistics- Simulation and Computation, 2014, 43(5): 947-968.
22	Li Jiaxin, Peng Ke, Wang Wenjie, et al. Optimization Design of Rockoons Based on Improved Sequential Approximation Optimization[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2022, 236(1): 140-153.
23	Han Zhonghua, Görtz Stefan. Hierarchical Kriging Model for Variable-fidelity Surrogate Modeling[J]. AIAA Journal, 2012, 50(9): 1885-1896.
24	Han Zhonghua, Zhang Keshi. Surrogate-based Optimization[M]//Olympia Roeva. Real-world Applications of Genetic Algorithms. Rijeka: IntechOpen, 2012: Ch. 17.
25	Hewing Lukas, Kabzan Juraj, Zeilinger Melanie N. Cautious Model Predictive Control Using Gaussian Process Regression[J]. IEEE Transactions on Control Systems Technology, 2020, 28(6): 2736-2743.
26	华罗庚, 王元. 数论在近代分析中的应用[M]. 北京: 科学出版社, 1978.
27	Wang Dan, Lin Jiayu, Wang Yuangen. Query-efficient Adversarial Attack Based on Latin Hypercube Sampling[C]//2022 IEEE International Conference on Image Processing (ICIP). Piscataway: IEEE, 2022: 546-550.
28	Tao Ye, Ferranti Francesco, Nakhla M S. Uncertainty Quantification Using Parameter Space Partitioning[J]. IEEE Transactions on Microwave Theory and Techniques, 2021, 69(4): 2110-2119.
29	Li Ji, Yuesong Nan, Ji Hui. Un-supervised Learning for Blind Image Deconvolution via Monte-carlo Sampling[J]. Inverse Problems, 2022, 38(3): 035012.
30	Tan Wei, Hu Yongjiang, Zhao Yuefei, et al. Mission Planning for Unmanned Aerial Vehicles Based on Voronoi Diagram-tabu Genetic Algorithm[J]. Wireless Communications and Mobile Computing, 2021, 2021: 4154787.
31	Tao Ye, Ferranti Francesco, Nakhla M. High-dimensional Variability Analysis via Parameters Space Partitioning[C]//2020 IEEE/MTT-S International Microwave Symposium (IMS). Piscataway: IEEE, 2020: 61-63.
32	Majumder Raja, Gouri Sankar Bhunia, Patra Poly, et al. Assessment of Flood Hotspot at a Village Level Using GIS-based Spatial Statistical Techniques[J]. Arabian Journal of Geosciences, 2019, 12(13): 409.
33	Barmuta Pawd, Lukasik Konstanty, Ferranti Francesco, et al. Load-pull Measurements Using Centroidal Voronoi Tessellation[C]//2017 89th ARFTG Microwave Measurement Conference (ARFTG). Piscataway: IEEE, 2017: 1-4.
34	Badger L. Generating the Measures of n-balls[J]. The American Mathematical Monthly, 2000, 107(3): 256-258.
35	Gionis Aristides, Mannila Heikki, Tsaparas P. Clustering Aggregation[J]. ACM Transactions on Knowledge Discovery from Data, 2007, 1(1): 4-es.
36	Rodriguez Alex, Laio Alessandro. Clustering by Fast Search and Find of Density Peaks[J]. Science, 2014, 344(6191): 1492-1496.
37	Chen Yewang, Tang Shengyu, Bouguila N, et al. A Fast Clustering Algorithm Based on Pruning Unnecessary Distance Computations in DBSCAN for High-dimensional Data[J]. Pattern Recognition, 2018, 83: 375-387.
38	Fu Limin, Medico Enzo. FLAME, a Novel Fuzzy Clustering Method for the Analysis of DNA Microarray Data[J]. BMC Bioinformatics, 2007, 8(1): 3.
39	Du Mingjing, Ding Shifei, Jia Hongjie. Study on Density Peaks Clustering Based on K-nearest Neighbors and Principal Component Analysis[J]. Knowledge-Based Systems, 2016, 99: 135-145.
40	Xu Chen, Su Zhengchang. Identification of Cell Types from Single-cell Transcriptomes Using a Novel Clustering Method[J]. Bioinformatics, 2015, 31(12): 1974-1980.
41	Hou Jian, Zhang Aihua, Qi Naiming. Density Peak Clustering Based on Relative Density Relationship[J]. Pattern Recognition, 2020, 108: 107554.
42	Liu Rui, Wang Hong, Yu Xiaomei. Shared-nearest-neighbor-based Clustering by Fast Search and Find of Density Peaks[J]. Information Sciences, 2018, 450: 200-226.
43	Ortiz-Jiménez Guillermo, Mireille El Gheche, Simou Effrosyni, et al. Forward-backward Splitting for Optimal Transport Based Problems[C]//ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE, 2020: 5405-5409.
44	Peter Timm J, Nelles Oliver. Fast and Simple Dataset Selection for Machine Learning[J]. Automatisierungstechnik, 2019, 67(10): 833-842.
45	Zhan Kun, Zhang Changqing, Guan Junpeng, et al. Graph Learning for Multiview Clustering[J]. IEEE Transactions on Cybernetics, 2018, 48(10): 2887-2895.
46	Mohd Shoaib Khan, Alamri Badriah AS, Mursaleen M, et al. Sequence Spaces M(ϕ) and N(ϕ) with Application in Clustering[J]. Journal of Inequalities and Applications, 2017, 2017(1): 63.
47	Dimple, Pradeep Kumar Singh, Rajput Jitendra, et al. Combination of Discretization Regression with Data-driven Algorithms for Modeling Irrigation Water Quality Indices[J]. Ecological Informatics, 2023, 75: 102093.
48	Spiliotis Evangelos, Nikolopoulos K, Assimakopoulos Vassilios. Tales from Tails: On the Empirical Distributions of Forecasting Errors and Their Implication to Risk[J]. International Journal of Forecasting, 2019, 35(2): 687-698.

数据集名称	规模(点个数)	簇数	对应文献
Aggregation	788	7	[35-39]
Flame	240	2	[40-42]
Two⁃moon	400	2	[43-46]

参数	Aggregation	Flame	Two moon
MSE	初始模型： 8.33×10^-4	初始模型：4.69×10^-3	初始模型：3.26×10^-3
MSE	最终模型： 6.24×10^-4	最终模型：3.94×10^-3	最终模型：4.93×10^-4
MAE	初始模型： 3.47×10^-1	初始模型：6.55×10^-1	初始模型：4.93×10^-1
MAE	最终模型： 3.34×10^-1	最终模型：6.53×10^-1	最终模型：3.05×10^-1
MedianAE	初始模型： 2.17×10^-1	初始模型：4.94×10^-1	初始模型：3.97×10^-1
MedianAE	最终模型： 2.15×10^-1	最终模型：3.65×10^-1	最终模型：1.28×10^-1

[1]	余洋, 夏雨星, 陆文韬, 刘霡, 高世轩, 陈东阳. 基于荷-储碳流模型的电力系统双层优化调度[J]. 系统仿真学报, 2024, 36(10): 2288-2299.
[2]	马宗方, 张琳旋, 宋琳, 王嘉. 基于改进动态窗口的车库AGV路径规划及仿真[J]. 系统仿真学报, 2024, 36(10): 2265-2276.
[3]	陈世康, 王智民, 梁标, 顾焜仁, 宋珂满, 高远. 面向体系仿真的蓝军舰艇编队防空作战指控建模方法[J]. 系统仿真学报, 2024, 36(10): 2246-2256.
[4]	杨哲, 崔颖函, 郭灵犀, 李嘉鑫, 吴旭生. 融合数据仿真与深度学习算法的飞行器残骸搜寻技术[J]. 系统仿真学报, 2024, 36(10): 2238-2245.
[5]	杨妹, 王鹏. 一种基于插件的虚幻引擎HLA分布式仿真适配器[J]. 系统仿真学报, 2024, 36(10): 2231-2237.
[6]	周芳, 范波, 刘小毅, 丁一珊, 张宁馨, 邵亚超, 翟小玉. 一种基于改进行为树的智能对手行为模拟技术[J]. 系统仿真学报, 2024, 36(9): 1995-2003.
[7]	徐丽霞, 钟季龙, 伍劭实, 丁一珊, 翟小玉, 陈世钊, 王鹥喆, 温雪, 曾隽芳, 侯新文. 基于云模型和最大均值差异的指标迁移学习[J]. 系统仿真学报, 2024, 36(9): 2004-2015.
[8]	姚万业, 庞泽伟, 孙沛杰, 王祝. 基于窗口化匹配估计的ORB-SLAM算法研究[J]. 系统仿真学报, 2024, 36(9): 2032-2042.
[9]	朱前坤, 李继武, 杜永峰. 老旧教学楼火灾场景人员疏散仿真研究[J]. 系统仿真学报, 2024, 36(9): 2043-2053.
[10]	赵佰亭, 施建国, 贾晓芬. 井筒提升机数字孪生系统研究[J]. 系统仿真学报, 2024, 36(9): 2054-2064.
[11]	李超, 李贾宝, 丁才昌, 叶志伟, 左方威. 基于DRL的边缘监控任务卸载与资源分配算法[J]. 系统仿真学报, 2024, 36(9): 2113-2126.
[12]	李淑庆, 宋易宵, 钟国剑. 考虑近邻度值之和的城市轨道网络抗毁性研究[J]. 系统仿真学报, 2024, 36(9): 2127-2136.
[13]	王越龙, 王松艳, 晁涛. 基于多步信息辅助的Q-learning路径规划算法[J]. 系统仿真学报, 2024, 36(9): 2137-2148.
[14]	徐健, 刘高峰, 赵一剑, 郑自立, 闫焕营. 装配机器人的数字孪生虚实同步及抓取方法[J]. 系统仿真学报, 2024, 36(9): 2181-2192.
[15]	钱殿伟, 齐红敏, 刘振, 周志明, 易建强. 基于改进近端策略优化的空战自主决策研究[J]. 系统仿真学报, 2024, 36(9): 2208-2218.