基于改进型生成对抗网络的指挥信息系统模拟数据生成算法

doi:10.3969/j.issn.1001-506X.2021.01.20

Abstract

Abstract:

Aiming at the lack of simulation training data in command information system, a simulation data generation algorithm based on evolutional generative adversarial networks (EGAN-SDG) is put forword, which constructs the index system of command information system's ability to fulfill missions and tasks. The mapping mode of unstructured information processing in command information system and the fitting factor to modify the loss function to improve the optimization performance are proposed. Simulation results show that the algorithm can generate data sets with high similarity distribution of the original data, and can provide data support for the simulation training of all elements of the command information system.

Key words: generative adversarial network, command information system, simulation data set

CLC Number:

TP391.9

Xiangxuan TIAN, Zhiqiang SHI. Simulation data generation algorithm based on evolutional generative adversarial networks for command information system[J]. Systems Engineering and Electronics, 2021, 43(1): 163-170.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Table 2

Fig.5

Fig.6

Fig.7

Fig.8

References 32

1	LI X, HUANG H. Reliability evaluation for the C4ISR communication system via propagation model[C]//Proc.of the Annual Reliability and Maintainability Symposium, 2019.
2	OUYANG S, DAI Z, YAN C. Operational effectiveness evaluation of maritime C4ISR system based on system dynamics[C]//Proc.of the 37th Chinese Control Conference, 2018: 8583-8588.
3	JIAO Z , YAO P . Capability construction of C4ISR based on AI planning[J]. IEEE Access, 2019, 1 (7): 31997- 32008.
4	XIAO B, LUO P, CHENG Z. Systematic combat effectiveness evaluation model based on xgboost[C]//Proc.of the 12th International Conference on Reliability Maintainability and Safety, 2018: 130-134.
5	HILL R, TOLK A. Open challenges in building combat simulation systems to support test, analysis and training[C]//Proc.of the Winter Simulation Conference, 2018: 3730-3741.
6	SHU Z , WANG W . Design of an optimized architecture for manned and unmanned combat system of systems: formulation and coevolutionary optimization[J]. IEEE Access, 2018, 1 (6): 52725- 52740.
7	HAI N, YUAN S. Research on integrated simulation training system for warship communication[C]//Proc.of the International Conference on Smart Grid and Electrical Automation, 2017: 533-537.
8	HARDER B, BLAIS C. Conceptual framework for an automated battle planning system in combat simulations[C]//Proc.of the Winter Simulation Conference, 2017: 4141-4152.
9	TOLT G, HEDSTROM J, BRUVOLL S. Multi-aspect path planning for enhanced ground combat simulation[C]//Proc.of the IEEE Symposium Series on Computational Intelligence, 2017.
10	XU X M , YANG R N . Situation assessment for air combat based on novel semi-supervised naive Bayes[J]. Journal of Systems Engineering and Electronics, 2018, 29 (4): 768- 779.
11	TOLK A. Tutorial on the engineering principles of combat modeling and distributed simulation[C]//Proc.of the Winter Simulation Conference, 2019: 18-32.
12	段萌, 王功鹏, 牛常勇. 基于卷积神经网络的小样本图像识别方法[J]. 计算机工程与设计, 2018, 39 (1): 224- 229.
	DUAN M , WANG G P , NIU C Y . Method of small sample size image recognition based on convolution neural network[J]. Computer Engineering and Design, 2018, 39 (1): 224- 229.
13	任佳, 高晓光. 信息不完备小样本条件下离散DBN参数学习[J]. 系统工程与电子技术, 2012, 34 (8): 1723- 1728.
	REN J , GAO X G . Discrete dynamic BN parameter learning under small sample and incomplete information[J]. Systems Engineering and Electronics, 2012, 34 (8): 1723- 1728.
14	常沙, 赵旭赟. 面向战役模拟训练的气象水文环境想定生成研究[J]. 军事运筹与系统工程, 2019, 33 (2): 29- 34.
	CHANG S , ZHAO X Z . Research on meteorological and hydrological environment scenario generation for campaign simu-lation training[J]. Military Operations Research and Systems Engineering, 2019, 33 (2): 29- 34.
15	LI H Z, CHEN F, YANG Z J. Bayesian reliability assessment method for single NC machine tool under zero failures[C]//Proc.of the International Conference on Applications and Techniques in Information Security, 2015: 662-666.
16	杨懿男, 齐林海, 王红. 基于生成对抗网络的小样本数据生成技术研究[J]. 电力建设, 2019, 40 (5): 71- 77.
	YANG Y N , QI L H , WANG H . Research on generation technology of small sample data based on generative adversarial network[J]. Electric Power Construction, 2019, 40 (5): 71- 77.
17	胡晓峰, 贺筱媛, 饶德虎. 基于复杂网络的体系作战指挥与协同机理分析方法研究[J]. 指挥与控制学报, 2015, 1 (1): 5- 13.
	HU X F , HE X Y , RAO D H . Research on mechanism of command and coordination for system of systems operation based on complex network theory[J]. Journal of Command and Control, 2015, 1 (1): 5- 13.
18	WAN H. Deep learning neural network optimizing method and libraries review[C]//Proc.of the International Conference on Robots & Intelligent System, 2019: 497-500.
19	BRUZZONE L. Multisource labeled data: an opportunity for training deep learning networks[C]//Proc.of the International Geoscience and Remote Sensing Symposium, 2019: 4799-4802.
20	YUAN X , HE P . Adversarial examples: attacks and defenses for deep learning[J]. IEEE Trans.on Neural Networks and Learning Systems, 2019, 30 (9): 2805- 2824.
21	GOODFELLOW I, POUGET A J. Generative adversarial nets[C]//Proc.of the Conference on Advances in Neural Information Processing Systems, 2014: 2672-2680.
22	GOODFELLOW I , BENGIO Y , COURVILLE A . Deep lear-ning[M]. Cambridge: MIT Press, 2016.
23	MEHTA K, KOBTI Z. Data augmentation using CA evolved GANs[C]//Proc.of the Symposium on Computers and Communications, 2019: 1087-1092.
24	ABRAMIAN D, EKLUND A. Refacing: reconstructing anonymized facial features using GANS[C]//Proc.of the 16th International Symposium on Biomedical Imaging, 2019: 1104-1108.
25	曹雷. 指挥信息系统[M]. 2版北京: 国防工业出版社, 2016.
	CAO L . C4ISR System[M]. 2nd ed Beijing: National Defense Industry Press, 2016.
26	曹雷, 张永亮. 以知识为中心的未来指挥信息系统概念、能力及关键技术研究[J]. 军事运筹与系统工程, 2015, 29 (3): 69- 74.
	CAO L , ZHANG Y L . Research on the concept, capability and key technology of future command information system based on knowledge[J]. Military Operations Research and Systems Engineering, 2015, 29 (3): 69- 74.
27	中国人民解放军装甲兵工程学院.一种指挥信息系统与作战仿真系统数据交互转换器[P].中国:
	CN201410493974.8, 2015.01.28. Armored Engineering Collenge of PLA. A data exchange converter between command information system and combat simulation system[P].China: CN201410493974.8, 2015.01.28.
28	TRIPATHI A, RATHEE G. Taxonomy of missing data along with their handling Methods[C]//Proc.of the 5th International Conference on Image Information Processing, 2019: 463-468.
29	CHAKRABORTY G , CHAKRABORTY B . A novel normalization technique for unsupervised learning in ANN[J]. IEEE Trans.on Neural Networks, 2000, 11 (1): 253- 257.
30	PAN H , KONG L . A modified neighborhood mutual information and light gradient boosting machine-based long-term prediction approach for anode effect[J]. Measurement Science and Technology, 2019, 30 (11): 105- 120.
31	DANG X , SANG H L , BEKNAZARYAN A . On mutual information estimation for mixed-pair random variables[J]. Statistics & Probability Letters, 2018, 12 (24): 9- 16.
32	SARAKIT P, THEERAMUNKONG T. Improving emotion classification in imbalanced YouTube dataset using SMOTE algorithm[C]//Proc.of the 2nd International Conference on Advanced Informatics: Concepts, Theory and Applications, 2015.

ID	22维训练数据
ID	1_1	1_2	1_3	1_4	1_5	1_6	1_7	1_8	1_9	1_10	…	1_22
1	21	12.9	-	-	42.9	-	60.7	-	80	-	…	13.2
2	13.4	8.6	2.42	0.6	5.53	187	52.8	1.1	44	4.31	…	35.3
3	19.6	9.3	3.02	0.5	0.4	293	54.7	1.61	48	-	…	58.1
4	15.4	18.6	1.7	0.44	1.3	140	57.5	0.8	117	-	…	24.5
5	15.5	4.9	2.47	1.03	1.65	135	59.6	1.64	73	-	…	51.4
6	13.1	13.7	2.95	0.49	20.6	258	49.9	0.73	38	-	…	36.3
7	15.5	7	2.72	0.53	0.51	326	62.7	0.7	73	3.1	…	71.5
8	42.5	10.3	4.17	0.53	0.91	174	63.6	1.68	146	-	…	16.3
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	$ \ddots $	⋮

序号	1	2	3	4	5	6	7	…	22
1	1.00	0.21	0.33	0.34	0.25	0.33	0.34	…	0.18
2	0.21	1.00	0.15	0.21	0.24	0.20	0.15	…	0.48
3	0.33	0.15	1.00	0.64	0.25	0.57	0.60	…	0.18
4	0.34	0.21	0.64	1.00	0.21	0.60	0.65	…	0.22
5	0.25	0.24	0.25	0.21	1.00	0.21	0.19	…	0.26
6	0.33	0.20	0.57	0.60	0.21	1.00	0.64	…	0.23
7	0.34	0.15	0.60	0.65	0.19	0.64	1.00	…	0.15
8	0.24	0.22	0.18	0.26	0.31	0.22	0.24	…	0.28
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	$ \ddots $	⋮
22	0.18	0.48	0.18	0.22	0.26	0.23	0.15	…	1.00

[1]	Rongxiang LIU, Lin WU, Zhige XIE, Honglin LIU. Auxiliary situation analysis for air defense system based on generative adversarial network [J]. Systems Engineering and Electronics, 2022, 44(8): 2522-2529.
[2]	Bowei QIN, Lei JIANG, Hua XU, Zisen QI. Modulation recognition algorithm based on residual generation adversarial network [J]. Systems Engineering and Electronics, 2022, 44(6): 2019-2026.
[3]	Pingliang XU, Yaqi CUI, Wei XIONG, Zhenyu XIONG, Xiangqi GU. Generative track segment consecutive association method [J]. Systems Engineering and Electronics, 2022, 44(5): 1543-1552.
[4]	Kai SHAO, Miaomiao ZHU, Guangyu WANG. Modulation recognition method based on generative adversarial andconvolutional neural network [J]. Systems Engineering and Electronics, 2022, 44(3): 1036-1043.
[5]	Pengyu CAO, Chengzhi YANG, Limeng SHI, Hongchao WU. LPI radar signal enhancement based on DAE-GAN network [J]. Systems Engineering and Electronics, 2021, 43(9): 2493-2500.
[6]	Fan ZHAO, Hu JIN. Communication jamming waveform generation technology based on GAN [J]. Systems Engineering and Electronics, 2021, 43(4): 1080-1088.
[7]	Nan XIANG, Xiongtao ZHANG, Yajie DOU, Xiangqian XU, Kewei YANG, Yuejin TAN. High-end equipment development data hyposensitization method based on generative adversarial networks [J]. Systems Engineering and Electronics, 2020, 42(6): 1310-1316.
[8]	Xiaojing YIN, Xiaofeng HU, Rongxiang LIU, Zhige XIE, Jun MA. Generative method of SOS capability maps based on GAN [J]. Systems Engineering and Electronics, 2020, 42(10): 2257-2264.
[9]	Kefan ZHU, Jiegui WANG. Low-resolution radar target one-step recognition algorithm based on SCGAN+CNN with a limited training data [J]. Systems Engineering and Electronics, 2020, 42(1): 67-75.
[10]	HAO Yunfei, LIU Zhangmeng, GUO Fucheng, ZHANG Min. Open-set recognition of signal modulation based on generative adversarial networks [J]. Systems Engineering and Electronics, 2019, 41(11): 2619-2624.

Simulation data generation algorithm based on evolutional generative adversarial networks for command information system

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 32

Related Articles 10

Recommended Articles

Metrics

Comments