基于D-S证据理论的导弹制导控制系统的联合最小二乘支持向量机预测模型

doi:10.3969/j.issn.1000-1093.2015.08.013

兵工学报 ›› 2015, Vol. 36 ›› Issue (8): 1466-1472.doi: 10.3969/j.issn.1000-1093.2015.08.013

基于D-S证据理论的导弹制导控制系统的联合最小二乘支持向量机预测模型

丛林虎¹，徐廷学¹，荀凯²

(1.海军航空工程学院兵器科学与技术系，山东烟台 264001； 2.91980部队，山东烟台 264321）

收稿日期:2014-08-14 修回日期:2014-08-14 上线日期:2015-10-16
通讯作者: 丛林虎 E-mail:xtxyt@163.com
作者简介:丛林虎（1986—），男，博士研究生
基金资助:
武器装备预先研究项目（40108）

ULS-SVM Prediction Model of Missile Guidance and Control Systems Based on D-S Evidence Theory

CONG Lin-hu¹， XU Ting-xue¹， GOU Kai²

(1.Department of Ordnance Science and Technology，Naval Aeronautical and Astronautical University，291980 Unit of PLA，Yantai 264321，Shandong，China）

Received:2014-08-14 Revised:2014-08-14 Online:2015-10-16
Contact: CONG Lin-hu E-mail:xtxyt@163.com

摘要/Abstract

摘要： 针对导弹制导控制系统电子设备密集、各性能特征参数间相互耦合关联性强、使用传统最小二乘支持向量机（LS-SVM）预测精度不高的问题，通过分析特征参数的时间相关性与空间相关性，对传统LS-SVM进行了改进，并利用D-S证据理论在数据融合中的优势，将传统与改进的LS-SVM进行融合，建立了联合最小二乘支持向量机（ULS-SVM）预测模型。以导弹制导控制系统为例，实现了关键参数预测。结果验证了模型的合理性与有效性。

关键词: 兵器科学与技术, D-S证据理论, 导弹, 预测模型, 最小二乘支持向量机

Abstract: For intense electronic equipment in missile guidance and control systems, coupling relationship among feature parameters and low prediction accuracy of traditional least squares support vector machine（LS-SVM），the traditional LS-SVM is improved by analyzing the temporal correlation and spatial correlation of feature parameters. Traditional LS-SVM and improved LS-SVM are fused by taking advantage of D-S evidence theory. An unification of least squares support vector machine（ULS-SVM）prediction model is established. The key parameters are predicted by taking missile guidance and control systems as an example. The results show that the proposed ULS-SVM prediction model is rational and effective.

Key words: ordnance science and technology, D-S evidence theory, missile, prediction model, least squares support vector machine

中图分类号:

TJ760

丛林虎，徐廷学，荀凯. 基于D-S证据理论的导弹制导控制系统的联合最小二乘支持向量机预测模型[J]. 兵工学报, 2015, 36(8): 1466-1472.

CONG Lin-hu， XU Ting-xue， GOU Kai. ULS-SVM Prediction Model of Missile Guidance and Control Systems Based on D-S Evidence Theory[J]. Acta Armamentarii, 2015, 36(8): 1466-1472.

参考文献

［1］孟秀云. 导弹制导与控制系统原理[M]. 北京：北京理工大学出版社，2004.
MENG Xiu-yun. The principle of missile guidance and control systems[M]. Beijing: Beijing Institute of Technology Press,2004. (in Chinese)
[2] 丛林虎，徐廷学，杨继坤，等. 导弹退化故障预测方法研究[J]．电光与控制，2014，21(5)：78-82.
CONG Lin-hu, XU Ting-xue, YANG Ji-kun, et al. A method for missile degradation fault prediction[J]. Electronics Optics & Control, 2014, 21(5): 78-82.(in Chinese)
[3] 方甲永. 复杂航空电子装备故障诊断与综合预测研究[D]．西安：空军工程大学，2011.
FANG Jia-yong. Study onfault diagnosis and integrated prognosis of complicated avionics[D]. Xi’an: Air Force Engineering University, 2011.(in Chinese)
[4] 刘文法，王旭，张建邦．基于LS-SVM的装备需求时间序列预测[J]．弹箭与制导学报，2006，26(1):780-783.
LIU Wen-fa, WANG Xu, ZHANG Jian-bang. Timeseries prediciton of military equipment based on least square support vector machine[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2006, 26(1):780-783. (in Chinese)
[5] Begg R K, Palaniswami M, Owen B. Support vector machines for automated gait classification[J]. IEEE Transactions on Biomedical Engineering, 2005, 52(5): 828-838.
[6] Doumpos M, Zopounidis C. Additive support vector machines for pattern classification[J]. IEEE Transactions on Systems, Man, and Cybernetics: Part B, 2007, 37(3): 540-550.
[7] 唐杰明，刘俊勇，杨可．基于灰色模型和最小二乘支持向量机的电力短期负荷组合预测[J]．电网技术，2009，33(3):63-68.
TANG Jie-ming, LIU Jun-yong, YANG Ke. Short-term load combination forecasting by grey model and least square support vector machine[J]. Power System Technology, 2009, 33(3):63-68. (in Chinese)
[8] Suykens J A K, Vandewalle J. Recurrent least squares support vector machines[J]. IEEE Transactions on Circuits and Systems, 2000, 47(7): 1109-1114.
[9] 张军，单永海，曹殿广, 等. 基于最小二乘支持向量机的机枪加速寿命建模[J]．兵工学报，2012，33(1)：63-68.
ZHANG Jun, SHAN Yong-hai,CAO Dian-guang, et al. Accelerated life modeling machine gun based on LS-SVM[J]. Acta Armamentarii, 2012, 33(1): 63-68.(in Chinese)
[10] 何友, 王国宏, 关欣,等. 多传感器信息融合及应用[M]. 北京: 电子工业出版社, 2010.
HE you, WANG Guo-hong, GUAN Xin, et al. Information fusion and application based on multi-sensor[M].Beijing: Publishing House of Electronics Industry,2010.(in Chinese)
[11] 郭华伟，施文康，刘清坤,等．一种新的证据组合规则[J]．上海交通大学学报，2006，40(11):1895-1900.
GUO Hua-wei, SHI Wen-kang, LIU Qing-kun, et al. A new combination rule of evidence[J]. Journal of Shanghai Jiaotong University, 2006, 40(11):1895-1900.(in Chinese)
[12] Yager R R. On the D-S framework and new combination rules[J]. Information Science, 1987, 41(2): 93-138.
[13] 丛林虎，徐廷学，董琪，等. 基于改进证据理论的导弹状态评价方法[J]．科技导报，2013，31(30)：15-18.
CONG Lin-hu, XU Ting-xue, DONG Qi,et al. Missile condition assessment based on improved evidence theory[J]. Science & Technology Review, 2013, 31(30): 15-18.(in Chinese)
[14] 权文，王晓丹，史朝辉，等. 多源不确定性信息融合中的冲突证据快速合成方法[J]．系统工程与电子技术，2012，34(2)：333-336.
QUAN Wen, WANG Xiao-dan, SHI Chao-hui,et al. Fast combination method of conflict evidence in multi-source uncertain information fusion[J]. Systems Engineering and Electronics, 2012, 34(2): 333-336. (in Chinese)
[15] Vatsa M，Singh R, Noore A. Unification of evidence-theoretic fusion algorithms: a case study in level-2 and level-3 fingerprint features[J]. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 2009,39(1): 47-56.
[16] Kwork J T Y. The evidence framework applied to support vector machines[J]. IEEE Transactions on Neural Networks, 2000,11(5): 1162-1173.
[17] Gestel T V, Suykens J A K, Baestaens, D E, et al. Financial time series prediction using least squares support vector machines within the evidence framework[J]. IEEE Transactions on Neural Networks, 2001,12(4): 809-821.
[18] 胡昌华，司小胜，史小华. 基于ER的陀螺漂移组合预测模型[J]．控制与决策，2009，24(2)：202-205.
HU Chang-hua, SI Xiao-sheng, SHI Xiao-hua.Gyroscopic drift combination forecasting model based on evidential reasoning[J]. Control and Decision, 2009, 24(2): 202-205.(in Chinese)
[19] 顾煜炯. 发电设备状态维修理论与技术[M]. 北京：中国电力出版社，2009.
GU Yu-jiong. Theories and techniques for condition maintenance of the power equipment[M]. Beijing: China Electricity Press,2009. (in Chinese)

[1]	宋羽，邹汝平，王军. 基于模型的系统工程在导弹系统研制中的实践[J]. 兵工学报, 2022, 43(S1): 97-106.
[2]	魏武臣，徐硕，何晓夫，王晓光，岳通. 基于作战仿真推演的空-地制导弹药效能评估[J]. 兵工学报, 2022, 43(S1): 107-114.
[3]	邹汝平，陈士超，张琨，李鹏，黄超凡. 一种适用于系列化空地导弹的飞行控制器设计方法[J]. 兵工学报, 2022, 43(S1): 121-127.
[4]	辛大钧，薛琨. 基于人工神经网络的非球形破片阻力系数预测模型[J]. 兵工学报, 2022, 43(5): 1083-1092.
[5]	杨建新，兰小平，冯亚东，杨一铭，郭志明. 基于改进樽海鞘群和最小二乘支持向量机算法的新型弹药质量评估方法[J]. 兵工学报, 2022, 43(5): 1012-1022.
[6]	刘月, 刘铁林, 姜相争, 韩月明. 基于径向基函数和自组织映射的导弹控制系统健康状态评估[J]. 兵工学报, 2022, 43(4): 931-939.
[7]	赵振，姜毅，刘相新，李玉龙，严松. 基于粒子法的柔性气缸导弹弹射数值仿真[J]. 兵工学报, 2022, 43(3): 533-541.
[8]	刘浩天，傅德彬，毕凤阳，杨华伟，卢丙举. 潜载导弹水下弹射筒口气泡数理模型[J]. 兵工学报, 2022, 43(3): 599-604.
[9]	张子豪, 王汉平. 考虑弹-筒柔性的潜载导弹弹射多体动力学分析方法[J]. 兵工学报, 2022, 43(2): 305-315.
[10]	周建平，李威，温求遒，夏群利，姜欢. 考虑滚仰导引头寄生回路的旋转导弹驾驶仪稳定性设计[J]. 兵工学报, 2022, 43(1): 1-10.
[11]	樊博璇，陈桂明，林洪涛. 弹道导弹中段反应式机动突防规避策略[J]. 兵工学报, 2022, 43(1): 69-78.
[12]	陈珂，孔永芳，符晓刚，王一波. 近程防空导弹绝缘气路接头的结构设计[J]. 兵工学报, 2022, 43(1): 79-85.
[13]	陈中原，韦文书，陈万春. 基于强化学习的多发导弹协同攻击智能制导律[J]. 兵工学报, 2021, 42(8): 1638-1647.
[14]	宋贵宝，姜子劼，刘战，刘镇毓. 多型岸舰导弹作战单元部署优化模型[J]. 兵工学报, 2021, 42(4): 878-887.
[15]	张兵，侯明，王殿宇，董友亮. 迎角对导弹初始弹射弹道的影响[J]. 兵工学报, 2021, 42(2): 438-448.

基于D-S证据理论的导弹制导控制系统的联合最小二乘支持向量机预测模型

ULS-SVM Prediction Model of Missile Guidance and Control Systems Based on D-S Evidence Theory

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价