基于反馈特征提取和因果反演辨识的自主智能系统效能评估方法

doi:10.12305/j.issn.1001-506X.2026.01.19

Abstract

Abstract:

To solve the problem of massive data feature overload and anti-causal data in real and simulated tests, a feature extraction method is proposed based on feedback closed-loop strategy. Firstly, extracts features from the original data space in reverse based on traditional feature reduction. Secondly, data identification method based on anti-causality identification, which identifies and excludes anti-causality data by calculating the consistency of causal logical relationships of data. Finally, the verification results using unmanned systems as an example show that, while retaining only 54.14% of the original information, system performance evaluation error reduced by approximately 12.43%. The method proposed has strong generalization ability, model independence and robustness.

Key words: feedback-based attribute extraction, anti-causality identification, autonomous intelligent system, performance evaluation

CLC Number:

TP 18

Chenhao YU, Leilei CHANG, Yu ZHOU, Jianbin SUN. Performance evaluation method for autonomous intelligent system using feedback-based attribute extraction and anti-causality[J]. Systems Engineering and Electronics, 2026, 48(1): 209-217.

Figures/Tables 9

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Table 2

Fig.6

Table 3

References 30

1	CHEN J, SUN J, WANG G. From unmanned systems to autonomous intelligent systems[J]. Engineering, 2022, 12, 16- 19. doi: 10.1016/j.eng.2021.10.007
2	TAN Y W, WANG J D, LIU J J, et al. Unmanned systems security: models, challenges, and future directions[J]. IEEE Network, 2020, 34 (4): 291- 297. doi: 10.1109/MNET.001.1900546
3	MOHSAN S A H, KHAN M A, NOOR F, et al. Towards the unmanned aerial vehicles （UAVs）: a comprehensive review[J]. Drones, 2022, 6 (6): 147. doi: 10.3390/drones6060147
4	WANG H F, CHENG H J, HAO H Y. The use of unmanned aerial vehicle in military operations[C]//Proc. of the International Conference on Man-Machine-Environment System Engineerin, 2020.
5	HYLAND S L, FALTYS M, HUSER M, et al. Early prediction of circulatory failure in the intensive care unit using machine learning[J]. Nature Medicine, 2020, 26, 364- 373. doi: 10.1038/s41591-020-0789-4
6	BAKKER B, ZABłOCKI B, BAKER A, et al. A multi-stage, multi-feature machine learning approach to detect driver sleepiness in naturalistic road driving conditions[J]. IEEE Trans. on Intelligent Transportation Systems, 2021, 23 (5): 4791- 4800.
7	FU S X, LI H X, LIU Y, et al. Social media overload, exhaustion, and use discontinuance: examining the effects of information overload, system feature overload, and social overload[J]. Information Processing & Management, 2020, 57 (6): 102307.
8	YANG M J, LIM S Y, PARK H J, et al. Solving the data overload: device-to-device bearer control architecture for cellular data offloading[J]. IEEE Vehicular Technology Magazine, 2013, 8 (1): 31- 39. doi: 10.1109/MVT.2012.2234052
9	PAWLOWSKI N, CASTRO D C, GLOCKER B. Deep structural causal models for tractable counterfactual inference[J]. Advances in Neural Information Processing Systems, 2020, 33, 857- 869.
10	ANOGUEIR A R, GAMA J, FERREIRA C A. Causal discovery in machine learning: theories and applications[J]. Journal of Dynamics & Games, 2021, 8 (3): 203- 231.
11	PROSPERI M, GUO Y, SPERRIN M, et al. Causal inference and counterfactual prediction in machine learning for actionable healthcare[J]. Nature Machine Intelligence, 2020, 2, 369- 375. doi: 10.1038/s42256-020-0197-y
12	POPESCU D C, DUMITRACHE I. Knowledge representation and reasoning using interconnected uncertain rules for describing workflows in complex systems[J]. Information Fusion, 2023, 93, 412- 428. doi: 10.1016/j.inffus.2023.01.007
13	WANG J R, LIU Y, LI P G, et al. Overview of data quality: examining the dimensions, antecedents, and impacts of data quality[J]. Journal of the Knowledge Economy, 2024, 15, 1159- 1178. doi: 10.1007/s13132-022-01096-6
14	HERNAN M A, WANG W, LEAF D E. Target trial emulation: a framework for causal inference from observational data[J]. JAMA, 2022, 328 (24): 2446- 2447. doi: 10.1001/jama.2022.21383
15	CHANG L L, ZHOU Z J, YOU Y, et al. Belief rule based expert system for classification problems with new rule activation and weight calculation procedures[J]. Information Sciences, 2016, 336, 75- 91. doi: 10.1016/j.ins.2015.12.009
16	HOU Z Q, MARTINEZ-GARCIA M, ZHANG Y, et al. Entropy measures in machine fault diagnosis: Insights and applications[J]. IEEE Trans. on Instrumentation and Measurement, 2020, 69 (6): 2607- 2620. doi: 10.1109/TIM.2020.2981220
17	SANG B B, CHEN H M, YANG L, et al. Incremental feature selection using a conditional entropy based on fuzzy dominance neighborhood rough sets[J]. IEEE Trans. on Fuzzy Systems, 2021, 30 (6): 1683- 1697.
18	BEIRANVAND F, MEHRDAD V, DOWLATSHAHI M B. Unsupervised feature selection for image classification: a bipartite matching-based principal component analysis approach[J]. Knowledge-Based Systems, 2022, 250, 109085. doi: 10.1016/j.knosys.2022.109085
19	ANOWAR F, SADAOUI S, SELIM B. Conceptual and empirical comparison of dimensionality reduction algorithms （PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE）[J]. Computer Science Review, 2021, 40, 100378. doi: 10.1016/j.cosrev.2021.100378
20	LIANG X P, TANG Z J, WU J L, et al. Robust image hashing with isomap and saliency map for copy detection[J]. IEEE Trans. on Multimedia, 2021, 25, 1085- 1097.
21	WU X, HONG D F, CHANUSSOT J. Convolutional neural networks for multimodal remote sensing data classification[J]. IEEE Trans. on Geoscience and Remote Sensing, 2021, 60
22	张效康. 地理国情监测数据可靠性分析与控制方法研究[D]. 武汉: 武汉大学, 2017.
	ZHANG X K. Reliability analysis and controlling methods for national geographic state monitoring data [D]. Wuhan: Wuhan University, 2017.
23	YU C H, CHANG L L, XU X B, et al. Compatible multi-model output fusion for complex systems modeling via set operation-based focus data identification[J]. Journal of Computational Science, 2024, 82, 102423. doi: 10.1016/j.jocs.2024.102423
24	YU C H, ZHU J Y, CHANG L L, et al. A novel data credibility-centric multi-model-based complex systems modeling approach for UAV capability evaluation[J]. International Journal of Machine Learning and Cybernetics, 2025, 16, 2687- 2704. doi: 10.1007/s13042-024-02415-w
25	JAVED S, HASSAN A, AHMAD R, et al. State-of-the-art and future research challenges in uav swarms[J]. IEEE Internet of Things Journal, 2024, 11 (11): 19023- 19045. doi: 10.1109/JIOT.2024.3364230
26	陈士涛, 张海林. 基于作战网络模型的异构无人机集群作战能力评估[J]. 军事运筹与系统工程, 2019, 33 (1): 38- 43.
	CHEN S T, ZHANG H L. Evaluation of heterogeneous UAV cluster combat capability based on combat network model[J]. Military Operations Research and Systems Engineering, 2019, 33 (1): 38- 43.
27	赵蕊蕊, 于海跃, 游雅倩, 等. 无人集群试验评估现状及技术方法综述[J]. 系统工程与电子技术, 2024, 46 (2): 570- 585.
	ZHAO R R, YU H Y, YOU Y Q, et al. Review on current status and technology method of unmanned swarm test evaluation[J]. Systems Engineering and Electronics, 2024, 46 (2): 570- 585.
28	范波, 钟季龙, 徐丽霞, 等. 基于因果熵的无人集群对抗评估指标分配方法[J]. 系统工程与电子技术, 2024, 46 (6): 2034- 2043.
	FAN B, ZHONG J L, XU L X, et al. Index allocation method for unmanned swarm confrontation evaluation based on causal entropy[J]. Systems Engineering and Electronics, 2024, 46 (6): 2034- 2043.
29	SUN C R, FONTANESI G, CANBERK B, et al. Advancing UAV communications: a comprehensive survey of cutting-edge machine learning techniques[J]. IEEE Open Journal of Vehicular Technology, 2024, 5, 825- 854. doi: 10.1109/OJVT.2024.3401024
30	BAI L, LIANG J Y. K-relations-based consensus clustering with entropy-norm regularizers[J]. IEEE Trans. on Neural Networks and Learning Systems, 2024, 35 (12): 17662- 17673. doi: 10.1109/TNNLS.2023.3307158

模型	模型输入（训练集）			测试集结果MAE
模型	特征数量	数据量	输入信息百分比/%	测试集结果MAE
基准模型	16 （100%）	400 （100%）	100	范围	[0.0750, 0.1972]
				均值	0.1360
				方差	1.2416E-03
特征提取后模型	9 （56.25%）	400 （100%）	56.25	范围	[0.0864, 0.2058]
				均值	0.1388
				方差	1.3285E-03
数据辨识后模型	16 （100%）	385 （96.25%）	96.25	范围	[0.0541, 0.1669]
				均值	0.1110
				方差	7.9503E-04
特征提取+数据辨识后模型	9 （56.25%）	385 （96.25%）	54.14	范围	[0.0831, 0.1741]
				均值	0.1191
				方差	4.9817E-04

不同方法	训练集输入		测试集结果（MAE）
不同方法	特征数量	数据量	测试集结果（MAE）
基准模型	16	400	均值	0.1360
特征选择信息熵	9	400	均值	0.1510
特征选择Isomap	9	400	均值	0.1530
数据辨识（排除5组）	16	395	均值	0.1402
数据辨识（排除15组）	16	385	均值	0.1427
数据辨识（排除30组）	16	370	均值	0.1643
数据辨识（KRCC）	16	380	均值	0.1420

基准模型	模型	模型的输入（训练集）				测试集MAE
基准模型	模型	特征数量	数据量	百分比/%	均值	范围	方差	变化/%
BPNN	Baseline	16	400	100	0.1360	[0.0750,0.1972]	1.2416E-03	—
	Model_att	9	400	56.25	0.1388	[0.0864,0.2058]	1.3285E-03	2.06
	Model_quan	16	385	96.25	0.1110	[0.0541,0.1669]	7.9503E-04	−18.38
	Model_both	9	385	54.14	0.1191	[0.0831,0.1741]	4.9817E-04	−12.43
RBF	Baseline	16	400	100	0.1403	[0.0592,0.1991]	1.0032E-03	—
	Model_att	9	400	56.25	0.1441	[0.0976,0.1771]	4.3198E-04	2.71
	Model_quan	16	385	96.25	0.1171	[0.0687,0.1860]	8.3103E-04	−16.54
	Model_both	9	385	54.14	0.1227	[0.0611,0.1819]	5.9187E-04	−12.55
GPR	Baseline	16	400	100	0.1816	[0.1126,0.2346]	5.0565E-04	—
	Model_att	9	400	56.25	0.1858	[0.1460,0.2183]	7.0347E-04	2.31
	Model_quan	16	385	96.25	0.1603	[0.0975,0.2373]	5.4856E-04	−11.73
	Model_both	9	385	54.14	0.1655	[0.1040,0.1813]	3.9483E-04	−8.87

[1]	Fei GAO, Jiang WU, Jun MA, Jia LIU, Xuanmin ZHANG, Bin LI, Jidong MENG, Wenbo NIU, Hongxing DANG. Research on the overall technology of space-based distributed radar sea surface target tracking application [J]. Systems Engineering and Electronics, 2024, 46(8): 2667-2675.
[2]	Mingfei ZHAO, Rui XUE. Comprehensive evaluation method based on weighted processing for data transmission performance of UAV group [J]. Systems Engineering and Electronics, 2024, 46(12): 4248-4258.
[3]	Changxiao ZHAO, Ershuai LI, Feng HE, Peng WANG. Bandwidth allocation and optimization of time-sensitive traffic in TSN [J]. Systems Engineering and Electronics, 2022, 44(6): 2027-2034.
[4]	Yuting ZHANG, Miao XIAO, Liang ZHANG, Huaian ZHOU, Zheng LYU. EMC performance evaluation of mobile communication satellites based on near-field scanning [J]. Systems Engineering and Electronics, 2020, 42(9): 1897-1902.
[5]	Leiyu CHEN, Zhijie ZHOU, Shuaiwen TANG, You CAO. Weapon equipment performance evaluation method with multi-information fusion [J]. Systems Engineering and Electronics, 2020, 42(7): 1527-1533.
[6]	Dangfei PAN, Wenxue LIU, Jian GE, Hong YUAN, Chao YUAN. Phase noise estimation method for RF front-end circuit of GNSS receiver [J]. Systems Engineering and Electronics, 2020, 42(3): 536-543.
[7]	HE Huafeng, HE Yaomin, XU Yongzhuang. High performance evaluation of seeker measurement based on improved BP neural network [J]. Systems Engineering and Electronics, 2019, 41(7): 1544-1550.
[8]	ZHANG Denghui, MA Ping, CHAO Tao, WANG Songyan. Performance evaluation of guidance and control system for hypersonic vehicle [J]. Systems Engineering and Electronics, 2018, 40(8): 1811-1816.
[9]	QIN Maosen, ZHAO Danling, YANG Kewei. Effectiveness evaluation of anti-submarine activities based on combat network [J]. Systems Engineering and Electronics, 2018, 40(7): 1513-1520.
[10]	HE Weikun, GAO Li, WANG Xiaoliang, WU Renbiao. Wind farm clutters suppression for weather radar based on improved MAP method [J]. Systems Engineering and Electronics, 2018, 40(5): 1018-1025.
[11]	HE Zhaoyang, WANG Qianzhe, SONG Bowen, PENG Weidong, WANG Jinjiang. RF stealth performance evaluation method for radar signal waveform [J]. Systems Engineering and Electronics, 2017, 39(10): 2234-2238.
[12]	TAN Wei-kai，GUO Ai-huang， QIAN Ye-qing. Performance evaluation method for small cells based on FAHP [J]. Systems Engineering and Electronics, 2014, 36(8): 1651-1655.
[13]	JING Pei-liang， XU Shi-you， LI Xian， CHEN Zeng-ping. Performance evaluation of multiple target tracking:a survey [J]. Systems Engineering and Electronics, 2014, 36(11): 2127-2132.
[14]	YANG Wei，FU Yao-wen， LI Xiang. Performance evaluation of multi-object filter system based on the optimal assignment [J]. Systems Engineering and Electronics, 2013, 35(9): 1809-1814.
[15]	ZHOU Pu-cheng, WANG Feng, CUI Xunxue, XUE Mo-gen. Performance evaluation approach to image fusion based on ordering approximate to ideal solution [J]. Journal of Systems Engineering and Electronics, 2011, 33(3): 681-684.

Performance evaluation method for autonomous intelligent system using feedback-based attribute extraction and anti-causality

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 30

Related Articles 15

Recommended Articles

Metrics

Comments