基于改进粒子群优化极限学习机的弹丸参数辨识

doi:10.12305/j.issn.1001-506X.2023.02.24

摘要/Abstract

摘要：

针对随机产生输入权重和隐含层神经元阈值导致利用极限学习机辨识弹丸气动参数时会出现辨识结果发散问题, 本文将粒子群算法与极限学习机结合, 并且引入自适应更新策略以及粒子变异策略, 提出了一种自适应变异粒子群优化极限学习机算法。该算法利用自适应变异粒子群算法寻优产生极限学习机的输入权重和隐含层阈值, 有效改善算法性能。仿真实验表明，利用自适应变异粒子群优化极限学习机算法辨识弹丸气动参数, 精度高、收敛速度快, 能够充分满足实际工程需要。

关键词: 弹丸, 气动参数辨识, 极限学习机, 粒子群优化算法, 自适应更新策略, 粒子变异策略

Abstract:

In view of the identification results diverge when using extreme learning machine to identify the aerodynamic parameters of the projectile, due to the randomly generated input weights and hidden layer neuron thresholds, an adaptive mutation particle swarm optimization extreme learning machine algorithm is proposed. The paper introduces the adaptive update strategy and particle mutation strategy into particle swarm optimization algorithm and couples it with extreme learning machine. The proposed algorithm optimizes the input weights and hidden layer thresholds of extreme learning machine through adaptive mutation particle swarm optimization algorithm, the adaptive update strategy and particle mutation strategy in algorithm effectively improve the performance of the algorithm. Simulation experiments show that the use of adaptive mutation particle swarm optimization extreme learning machine exceedingly improve identification accuracy and convergence speed, which is practical in engineer application.

Key words: projectile, aerodynamic parameter identification, extreme learning machine, particle swarm optimization algorithm, adaptive update strategy, particle mutation strategy

中图分类号:

TJ412

夏悠然, 管军, 易文俊. 基于改进粒子群优化极限学习机的弹丸参数辨识[J]. 系统工程与电子技术, 2023, 45(2): 521-529.

Youran XIA, Jun GUAN, Wenjun YI. Projectile parameter identification: extreme learning machine optimized by improved particle swarm[J]. Systems Engineering and Electronics, 2023, 45(2): 521-529.

图/表 15

图1

图2

表1

表2

图3

表3

图4

图5

图6

图7

图8

图9

图10

图11

表4

参考文献 43

1	闫章更.射表技术[M].北京:国防工业出版社,2000.
	YANZ G.Firing table technology[M].Beijing:National Defense Industry Press,2000.
2	魏伟. 弹箭自由飞行试验数据辨识特征参数方法分析[D]. 南京: 南京理工大学, 2009.
	WEI W. Investigation on methods of characteristic parameters identification using free-flight range data for projectiles and rockets[D]. Nanjing: Nanjing University of Science and Technology, 2009.
3	蔡金狮.飞行器系统辨识学[M].北京:国防工业出版社,2006.
	CAIJ S.Aircraft system identification[M].Beijing:National Defense Industry Press,2006.
4	WARNER E P, NORTON F H. Preliminary report on free flight tests[R]. Washington: Government Printing Office, 1920.
5	方旭. 基于弹载组合测量的弹箭气动力参数辨识方法研究[D]. 南京: 南京理工大学, 2019.
	FANG X. Investigation on methods of characteristic parameters identification using free-flight range data for projectiles and rockets[D]. Nanjing: Nanjing University of Science and Technology, 2019.
6	苏浩秦,宋述杰,邓建华.适用于飞机舵面损伤情况下的一种在线辨识算法研究[J].西北工业大学学报,2005,23(3):316-320. doi: 10.3969/j.issn.1000-2758.2005.03.009
	SUH Q,SONGS J,DENGJ H.A better online identification algorithm with impairment of aircraft control surfaces considered[J].Journal of Northwestern Polytechnical University,2005,23(3):316-320. doi: 10.3969/j.issn.1000-2758.2005.03.009
7	KAMALIC,PASHILKARA,RAOLJ R.Evaluation of recursive least squares algorithm for parameter estimation in aircraft real time applications[J].Aerospace Science & Technology,2011,15(3):165-174.
8	杨云刚,赵军民,刘钧圣,等.基于最小二乘法的弹体参数辨识工程算法[J].弹箭与制导学报,2018,38(4):81-84.
	YANGY G,ZHAOJ M,LIUJ S,et al.Engineering algorithm of missile parameter identification based on least square method[J].Journal of Projectiles, Rockets, Missiles and Guidance,2018,38(4):81-84.
9	MUT M,LIZ,YANY,et al.Parameter identification of aircraft thin-walled structures using incomplete measurements[J].Journal of Vibroengineering,2012,14(2):602-610.
10	史忠科.梯度解耦的递推极大似然法及在飞行试验中的应用[J].西北工业大学学报,1995,13(4):581-586.
	SHIZ K.A new gradient-decoupled recursive maximum likelihood method and its application to flight test[J].Journal of Northwes-tern Polytechnical University,1995,13(4):581-586.
11	SHANTHAK N,JANARDHANAR N.Estimation of stability and control derivatives of light canard research aircraft from flight data[J].Defence Science Journal,2004,54(3):277-292. doi: 10.14429/dsj.54.2041
12	CARNDUFFS,COOKEA.Application of aerodynamic model structure determination to UAV data[J].Aeronautical Journal,2011,115(1170):481-492. doi: 10.1017/S0001924000006126
13	ZOUX S,LIC W.Maximum likelihood method based on interior point algorithm for aircraft parameter identification[J].Journal of Aircraft,2005,42,1355-1358. doi: 10.2514/1.15025
14	张天姣,钱炜祺,何开锋,等.基于最大似然法的风洞自由飞试验气动力参数辨识技术研究[J].实验流体力学,2015,29(5):8-14.
	ZHANGT J,QIANW Q,HEK F,et al.Research on aerodynamic parameter identification technology in wind tunnel free-flight test based on maximum likelihood estimation[J].Journal of Experiments in Fluid Mechanics,2015,29(5):8-14.
15	MAJEEDM,KARI N.Aerodynamic parameter estimation using adaptive unscented Kalman filter[J].Aircraft Engineering & Aerospace Technology,2013,85(4):267-279.
16	SHENJ L,SUY,LIANGQ,et al.Calculation and identification of the aerodynamic parameter for small-scaled fixed-wing UAVs[J].Sensors,2018,18(1):206.
17	罗鹏,杨华,陈伟芳.一种改进的扩展卡尔曼滤波气动参数辨识方法[J].工业控制计算机,2018,31(12):13-16. doi: 10.3969/j.issn.1001-182X.2018.12.006
	LUOP,YANGH,CHENW F.An improved method of extend Kalman filter for aerodynamic parameter identification[J].Industrial Control Computer,2018,31(12):13-16. doi: 10.3969/j.issn.1001-182X.2018.12.006
18	侯现钦,王良明,傅健.差分进化智能算法在高旋弹气动辨识中的应用[J].弹箭与制导学报,2020,40(3):103-107.
	HOUX Q,WANGL M,FUJ.Application of differential evolution intelligent algorithm in high-vibration aerodynamic identification[J].Journal of Projectiles, Rockets, Missiles and Guidance,2020,40(3):103-107.
19	MOHAMADA,KARIMIJ,NADERIA.Dynamic aerodynamic parameter estimation using a dynamic particle swarm optimization algorithm for rolling airframes[J].Journal of the Brazilian Society of Mechanical Sciences and Engineering,2020,42,579. doi: 10.1007/s40430-020-02658-y
20	韩建福,杜昌平,叶志贤,等.基于双BP神经网络的扑翼飞行器气动参数辨识[J].计算机应用,2019,39(S2):299-302.
	HANJ F,DUC P,YEZ X,et al.Identification of aerodynamic parameters of flapping-wing micro aerial vehicle based on double BP neural network[J].Journal of Computer Application,2019,39(S2):299-302.
21	RAMIREZ-MENDOZAA.Fuzzy adaptive neurons applied to the identification of parameters and trajectory tracking control of a multi-rotor unmanned aerial vehicle based on experimental aerodynamic data[J].Journal of Intelligent & Robotic Systems,2020,100(9):380-393. doi: 10.1007/s10846-020-01198-w
22	HUANGB,ZHUQ Y,SIEWC K.Extreme learning machine: theory and applications[J].Neurocomputing,2006,70(1/3):489-501.
23	林加润,殷建平,蔡志平,等.云计算环境下安全的极限学习机外包机制[J].计算机工程与科学,2015,37(10):1806-1810. doi: 10.3969/j.issn.1007-130X.2015.10.002
	LINJ R,YINJ P,CAIZ P,et al.Secure outsourcing of extreme learning machine in cloud computing[J].Computer Engineering & Science,2015,37(10):1806-1810. doi: 10.3969/j.issn.1007-130X.2015.10.002
24	KUSOKAA,BAEKS,MICHEY,et al.ELMVIS+: fast nonli-near visualization technique based on cosine distance and extreme learning machines[J].Neurocomputing,2016,205(12):247-263.
25	CHENC Q,VONGC M,WONGC M,et al.Efficient extreme learning machine via very sparse random projection[J].Soft Computing: A Fusion of Foundations Methodologies and Applications,2018,22(11):3563-3574.
26	LIANZ Y,DUANL J,QIAOY H,et al.The improved ELM algorithms optimized by bionic WOA for EEG classification of brain computer interface[J].IEEE Access,2021,9,67405-67416. doi: 10.1109/ACCESS.2021.3076347
27	CHENGJ,CHENJ,GUOY N,et al.Adaptive CCR-ELM with variable-length brain storm optimization algorithm for class-imba-lance learning[J].Natural Computing,2021,20(1):11-22. doi: 10.1007/s11047-019-09735-9
28	鲁迪,王星华,刘升伟,等.加权多分位鲁棒ELM的短期负荷预测方法[J].电力系统及其自动化学报,2020,32(3):33-38.
	LUD,WANGX H,LIUS W,et al.Weighted multi-quantile robust ELM for short-term load forecasting[J].Proceedings of the CSU-ESPA,2020,32(3):33-38.
29	KUMARK,BHALAJIN.A novel hybrid RNN-ELM architecture for crime classification[M].Chicago:American Library Association,2020.
30	YANGL,ZHAOQ.A novel PPA method for fluid pipeline leak detection based on OPELM and bidirectional LSTM[J].IEEE Access,2020,8,107185-107199.
31	JIANGS,YANGX Y,LINGX S.Evolutionary extreme learning machine optimized by quantum-behaved particle swarm optimization[J].Journal of System Simulation,2017,29(10):219-230.
32	刘振男,杜尧,韩幸烨,等.基于遗传算法优化极限学习机模型的干旱预测——以云贵高原为例[J].人民长江,2020,51(8):17-22.
	LIUZ N,DUY,HANX Y,et al.Drought prediction based on genetic algorithm-optimized extreme learning machine mo-del: case of Yunnan-Guizhou Plateau[J].Yangtze River,2020,51(8):17-22.
33	吴雨.基于模拟退火算法的改进极限学习机[J].计算机系统应用,2020,29(2):163-168.
	WUY.Improved extreme learning machine based on simulated annealing algorithm[J].Computer Systems & Applications,2020,29(2):163-168.
34	KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proc. of the IEEE International Conference on Neural Networks, 1995.
35	URRACAR,SODUPE-ORTEGAE,ANTONANZASJ,et al.Evaluation of a novel GA-based methodology for model structure selection: the GA-PARSIMONY-science direct[J].Neurocomputing,2018,27,9-17.
36	WUY,GONGM G,MAW P,et al.High-order graph matching based on ant colony optimization[J].Neurocomputing,2019,328,97-104.
37	CHEN S L, YUAN Y, WANG J. An adaptive latent factor model via particle swarm optimization for high-dimensional and sparse matrices[C]//Proc. of the IEEE International Conference on Systems, Man and Cybernetics, 2019.
38	HAFIZF,SWAINA,MENDESE M.Two-dimensional(2D) particle swarms for structure selection of nonlinear systems[J].Neurocomputing,2019,367,114-129.
39	LIY Y,XIAOJ J,CHENY Q,et al.Evolving deep convolutional neural networks by quantum behaved particle swarm optimization with binary encoding for image classification[J].Neurocomputing,2019,362,156-165.
40	GOUDARZIS,WANH H,ANISIM H,et al.ABC-PSO for vertical handover in heterogeneous wireless networks[J].Neurocomputing,2017,256,149-157.
41	DINGH J,GUX S.Hybrid of human learning optimization algorithm and particle swarm optimization algorithm with scheduling strategies for the flexible job-shop scheduling problem[J].Neurocomputing,2020,414,313-332.
42	WANGB F,LIS,GUOJ,et al.Car-like mobile robot path planning in rough terrain using multi-objective particle swarm optimization algorithm[J].Neurocomputing,2018,282,42-51.
43	ZENGN Y,WANGZ D,ZHANGH,et al.An improved particle filter with a novel hybrid proposal distribution for quantitative analysis of gold immunochromatographic strips[J].IEEE Trans.on Nanotechnology,2019,18,819-829.

参数	设定值
最大迭代次数	1 000
最大速度	0.3
最小速度	-0.3
最大速度惯性权重	0.8
最小速度惯性权重	0.2
个体认识系数	1.596 18
社会认知系数	1.832 45
方差判别阈值	0.03
变异概率	0.01

测试函数	算法	粒子数	最大值	最小值	平均值	平均收敛迭代次数	理论值
Sphere	PSO	50	0.515	0.336	0.130	676	0
		100	0.050	0.004	0.018	489	0
		150	0.006	0.001	0.002	396	0
	AMPSO	50	4.17×10^－37	2.89×10^－52	7.64×10^－40	441	0
		100	6.91×10^－42	8.52×10^－54	1.54×10^－46	313	0
		150	9.15×10^－47	2.61×10^－58	1.17×10^－51	245	0
Schaffer	PSO	50	0.037	0.008	0.011	718	0
		100	0.037	0.009	0.010	502	0
		150	0.009	1.98×10^－17	0.008	429	0
	AMPSO	50	0.009	0	6.55×10^－4	489	0
		100	0.009	0	8.27×10^－5	298	0
		150	0.000	0	0	264	0

参数	数值
初速/(m/s)	930
射角/(°)	45
射向/(°)	95.595
自转角速度/(rad/s)	188.5

算法	结构	辨识精度	辨识时间/s
ELM	5-70-7	3.34×10^－5	7.68
PSO-ELM	5-65-7	5.41×10^－10	18.23
AMPSO-ELM	5-45-7	7.17×10^－12	8.02

[1]	杨建峰, 肖和业, 李亮, 白俊强, 董维浩. 基于模糊聚类和专家评分机制的无人机多层次模块划分方法[J]. 系统工程与电子技术, 2022, 44(8): 2530-2539.
[2]	杨占刚, 徐海义, 成博源, 石旭东. 基于FWA-DBN的航空发电机偏心故障诊断[J]. 系统工程与电子技术, 2022, 44(5): 1757-1764.
[3]	杜思予, 全英汇, 沙明辉, 方文, 邢孟道. 基于进化PSO算法的稀疏捷变频雷达波形优化[J]. 系统工程与电子技术, 2022, 44(3): 834-840.
[4]	张捷, 杨丽花, 聂倩. 新型的基于堆栈式ELM的时变信道预测方法[J]. 系统工程与电子技术, 2022, 44(2): 662-667.
[5]	董庆, 李本威, 闫思齐, 钱仁军. 基于BSO-ELM的涡轴发动机加速过程性能参数预测[J]. 系统工程与电子技术, 2021, 43(8): 2181-2188.
[6]	史朝卫, 孟相如, 康巧燕, 苏玉泽. 基于混合流量预测的虚拟网络拓扑重构方法[J]. 系统工程与电子技术, 2021, 43(5): 1382-1388.
[7]	杨凌, 程丽, 韩琴, 赵傲男. 基于卡尔曼滤波的极限学习机在线盲均衡算法[J]. 系统工程与电子技术, 2021, 43(3): 623-630.
[8]	王坤, 侯树贤, 王力. 基于自适应变异PSO-SVM的APU性能参数预测模型[J]. 系统工程与电子技术, 2021, 43(2): 526-536.
[9]	赵帅, 刘松涛, 汪慧阳. 基于PSO-CNN的LPI雷达波形识别算法[J]. 系统工程与电子技术, 2021, 43(12): 3552-3563.
[10]	李玖阳, 胡敏, 王许煜, 徐家辉, 李菲菲. 基于ALPSO算法的低轨卫星小推力离轨最优控制方法[J]. 系统工程与电子技术, 2021, 43(1): 199-207.
[11]	何春蓉, 朱江. 基于注意力机制的GRU神经网络安全态势预测方法[J]. 系统工程与电子技术, 2021, 43(1): 258-266.
[12]	马愈昭, 王强强, 王瑞松, 熊兴隆. 基于SVD和MPSO-SVM的光纤周界[J]. 系统工程与电子技术, 2020, 42(8): 1652-1661.
[13]	苏志刚, 陈欣然, 郝敬堂. 基于粒子群优化的圆阵列波束形成方法[J]. 系统工程与电子技术, 2020, 42(7): 1449-1454.
[14]	吴明功, 叶泽龙, 温祥西, 王宏军. 基于局部弹性路由层的关键航路段改航规划[J]. 系统工程与电子技术, 2020, 42(7): 1534-1542.
[15]	孙胜祥, 韩霜. 基于双层决策的装备订购多因素激励定价模型与算法[J]. 系统工程与电子技术, 2020, 42(6): 1338-1347.