Situation Assessment Approach for Air Defense Operation System Based on Force-Sparsed Stacked-Auto Encoding Neural Networks

doi:10.16182/j.issn1004731x.joss.201803002

Abstract

Abstract: Aiming at the difficulty of feature extraction and related generation mechanism analysis for complex Air Defense System of Systems (ADSOS) using traditional data mining method, a novel situation assessment approach based on Force-Sparsed Stacked-Auto Encoding Neural Networks (FS-SAE) is proposed. Combined with big data and complex networks technology, FS-SAE situation assessment model is built. The emergence relations between the capacity indexes of ADSOS are formalized. And then, the formation mechanism and the contribution rate of capacity indexes are studied and the validity of this approach is validated by the simulation data. The experimental results show that formalized presentation for the emergence process of capacity indexes of ADSOS based on the proposed model not only reflects the complexity characteristics of non-linear and uncertainty in emergence process, but also gives general-defined meaning for indexes structure of ADSOS. It provides a feasible method for the commanders to deeply understand, manage and control the complex operation system.

Key words: situation assessment, heuristic knowledge, FS-SAE, emergence effect, contribution rate of SOS

CLC Number:

TP391.9

Guo Shengming, He Xiaoyuan, Wu Lin, Hu Xiaofeng. Situation Assessment Approach for Air Defense Operation System Based on Force-Sparsed Stacked-Auto Encoding Neural Networks[J]. Journal of System Simulation, 2018, 30(3): 772-785.

References 33

[1]	杜菲菲, 冯新喜. 用于态势评估的贝叶斯网络研究综述[J]. 电光与控制, 2010, 17(9): 42-46. Du F F, Feng X X. Survey on Bayesian Networks for situation Assessment[J]. Electronics Optics and Control, 2010, 17(9): 42-46.
[2]	唐雪松, 郭立红, 陈长喜. 基于案例推理方法在态势分析中的应用[J]. 计算机测量与控制, 2006, 14(12): 1723.Tang X S, Guo L H, Chen C X. Application of CBR method in situation assessment[J]. Computer measurement and control, 2006, 14(12): 1723.
[3]	孙永芹, 郑维广, 张振辉, 等. MFCMTA空战态势评估研究[J]. 计算机技术与发展, 2016, 26(6): 186-190.Sun Y Q, Zheng W G, Zhang Z H, et al. Study on situation assessment in MFCMTA Air Combat[J]. Computer technology and development, 2016, 26(6): 186-190.
[4]	王一村. 网络安全态势分析与预测方法研究[D]. 北京:北京交通大学, 2015.Wang Y C. Research on network security situation evaluation and forecast method[D]. Beijing: Beijing Jiaotong University, 2015.
[5]	王琳, 寇英信. Dempster-Shafer证据理论在空战态势评估方面的应用[J]. 电光与控制, 2007, 14(6): 155-157.Wang L, Kou Y X. Application of Dempster-Shafer evidence theory in air combat situation assessment[J]. Electronics Optics and Control, 2007, 14(6): 155-157.
[6]	王晓帆. 信息融合中的态势评估技术研究[D]. 西安: 西安电子科技大学, 2012.Wang X F. Research on situation assessment techniques in information fusion[D]. Xi’an: XiDian University, 2012.
[7]	胡晓峰. 战争工程论: 走向信息时代的战争方法学[M].北京: 国防大学出版社, 2013.Hu X F. On War System Engineering: Methodology towards to Information Age’s War[M]. Beijing: University of National Defense Press, 2013.
[8]	姚晓毅, 郭圣明, 胡晓峰, 等. 基于MINE—FNA组合算法的防空体系指标网构建方法研究[J]. 指挥控制与仿真, 2016, 1(1): 94-100.YAO X Y, GUO S M, HU X F, et al. Construction for Air Defense System of Systems based on MINE-FNA Assembled Algorithm[J]. Command Control & Simulation, 2016, 1(1): 94-100.
[9]	伍文峰, 胡晓峰. 基于大数据的网络化作战体系能力评估框架[J]. 军事运筹与系统工程, 2016, 30(2): 26-32.Wu W F, Hu X F. Capacity Evaluation of Networked Operational System of Systems based on Big Data[J]. Military Operations Research and Systems Engineering. 2016, 30(2): 26-32.
[10]	伍文峰, 胡晓峰, 郭圣明, 等. 基于多层时序网络的作战体系协同分析[J]. 复杂系统与复杂性科学, 2017, 14(2): 1-10.Wu W F, Hu X F, Guo S M, et al. Analysis of Combat SoS Coordination Based on Multi-Layered Temporal Networks[J]. Complex Systems & Complexity Science, 2017, 14(2): 1-10.
[11]	姚晓毅, 郭圣明, 胡晓峰, 等. 基于RF-GLR组合机器学习算法的防空体系能力指标挖掘方法研究[J]. 指挥与控制学报, 2015, 1(3): 269-277.YAO X Y, GUO S M, HU X F, et al. Design evaluation indices for Air defense systems based on RF-GLR integrated machine learning algorithms[J]. Journal of command and control, 2015, 1(3): 269-277.
[12]	Silver D, Huang A, Maddison C J, et al.Mastering the Game of Go with Deep Neural Networks and Tree Search[J]. Nature (S0028-0836), 2016, 529: 484-489.
[13]	Brown N, Sandholm T.Superhuman AI for heads-up no-limit poker: libratus beats top professionals[J]. Science (S0036-8075), 2017: 1733.
[14]	Elaraby N M, Elmogy M, Barakat S.Deep Learning: Effective Tool for Big Data Analytics[C]. International Journal of Computer Science Engineering (IJCSE), 2016: 254-262.
[15]	胡晓峰, 郭圣明, 贺筱媛. 指挥信息系统的智能化挑战—“深绿”计划及AlphaGo带来的启示与思考[J]. 指挥信息系统与技术, 2016, 7(3): 1-7.Hu X F, Guo S M, He X Y. Challenges to Intelligent Command Information System: Reason and Revelation on Deep Green Plan and AlphaGo[J]. Command Information System and Technology, 2016, 7(3): 1-7.
[16]	郭圣明, 贺筱媛, 胡晓峰, 等. 军用信息系统智能化的挑战与趋势[J]. 控制理论与应用, 2016, 33(12): 1562-1571.Guo S M, He X Y, Hu X F, et al. Challenges and Trends in Intelligent Military Information System[J]. Control Theory and Applications, 2016, 33(12): 1562-1571.
[17]	陶九阳, 吴琳, 胡晓峰. AlphaGo技术原理分析及人工智能军事应用展望[J]. 指挥与控制学报, 2016, 2(2): 114-120.Tao J Y, Wu L, Hu X F. Principle Analysis on AlphaGo and Perspective in Military Application of Artificial Intelligence[J]. Journal of Command and Control, 2016, 2(2): 114-120.
[18]	Goodfellow I, Bengio Y, Courville A.Deep learning[M]. MIT press, 2016.
[19]	Lee M, Bressler S, Kozma R.Advances in Cognitive Engineering Using Neural Networks[J]. Neural Networks the Official Journal of the International Neural Network Society (S0893-6080), 2017, 92: 1-2.
[20]	Chen Q, Song X, Yamada H, et al.Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference[C]. AAAI. 2016: 338-344.
[21]	Angermueller C, Pärnamaa T, Parts L, et al.Deep learning for computational biology[J]. Molecular Systems Biology (S1744-4292), 2016, 12(7): 878.
[22]	张乐, 刘忠, 张建强, 等. 基于自编码神经网络的装备体系评估指标简约方法[J]. 中南大学学报, 2013, 44(10): 4130-4137.Zhang L, Liu Z, Zhang J Q, et al. Reduction method of weapon system of systems assessment index system based on autoencoder[J]. Journal of Central South University, 2013, 44(10): 4130-4137.
[23]	欧微, 柳少军, 贺筱媛, 等. 基于时序特征编码的目标战术意图识别算法[J]. 指挥控制与仿真, 2016, 38(6): 36-41.Ou W, Liu S J, He X Y, et al. Tactical Intention Recognition Algorithm Based on Encoded Temporal features[J]. Command Control and Simulation, 2016, 38(6): 36-41.
[24]	Luong M T, Pham H, Manning C D.Effective Approaches to Attention-based Neural Machine Translation[C]. Empirical methods in natural language processing, 2015: 1412-1421.
[25]	Sukhbaatar S, Weston J, Fergus R.End-to-end memory networks[C]. Advances in neural information processing systems, 2015: 2440-2448.
[26]	Mnih V, Kavukcuoglu K, Silver D, et al.Human-level control through deep reinforcement learning[J]. Nature (S0028-0836), 2015, 518: 529-541.
[27]	David N R, Yakir A R, Hilary K F.Detecting Novel Associations in Large Data Sets[J]. Science (S0036-8075), 2011, 334(6062): 1518-1524.
[28]	Girvan M, Newman M E J. Community structure in social and biological networks[J]. Proceedings of the National Academy of Sciences, 2002, 99(12): 7821-7826.
[29]	Martinez A M, Kak A C.PCA versus LDA[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 2002, 23(2): 228-233.
[30]	Meng L, Ding S, Zhang N, et al.Research of stacked denoising sparse autoencoder[J]. Neural Computing & Applications (S0941-0643), 2016: 1-18.
[31]	BENGIO Y.Deep learning tutorial. http://deeplearning. net, LISA lab, University of Montreal, 2014.
[32]	Zhou X, Guo J, Wang S.Motion Recognition by Using a Stacked Autoencoder-Based Deep Learning Algorithm with Smart Phones[C]. International Conference on Wireless Algorithms, Systems, and Applications. Springer, Cham, 2015: 778-787.
[33]	Wu W, Hu X, Guo S, et al.Methods of Analyzing Combat SoS Coordination Pattern Based on Temporal Motif[C]. Asian Simulation Conference. Springer Singapore, 2016: 544-554.