基于极限学习机的武器装备作战效能全局敏感性分析
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摘要
作战效能是衡量武器有效性的关键指标。通过寻找影响作战效能的敏感性指标来提高武器装备的作战效能是一种简单有效的方法。为解决复杂评估模型计算成本高、计算时间缓慢的问题,本文引入极限学习机作为代理模型,替代复杂的效能评估模型。运用基于方差的全局敏感性分析,找到影响武器作战效能的敏感指标,进而找到与其关联的武器设备,对其功能进行完善和提高,从而提高武器的作战效能。本文以潜艇典型作战任务为作战效能敏感性分析的案例,分别与基于前馈神经网络模型、支持向量回归模型为代理模型的全局敏感性分析进行对比,验证该模型的有效性和高效性。
Operational effectiveness is a key indicator to measure the effectiveness of weapons. It is a simple and effective way to improve the operational effectiveness of weapons by finding operational effectiveness sensitive indicators. In order to solve the problems of high computation cost and low computation velocity of complex evaluation model,this paper introduces the extreme learning machine as the agent model to replace the complex performance evaluation model. To improve the operational effectiveness of weapons,this paper uses variance-based global sensitivity analysis to find the key factors that affect the effectiveness of weapons,and then find the equipment associated with its function to be improved. This paper uses the typical combat mission of the submarine as the effectiveness optimization case to evaluate the feasibility of the method. Compared with the global sensitivity analysis based on the feed-forward neural network agent model and the support vector regression model,the experimental results verify the validity and efficiency of L-EML model.
Operational effectiveness is a key indicator to measure the effectiveness of weapons. It is a simple and effective way to improve the operational effectiveness of weapons by finding operational effectiveness sensitive indicators. In order to solve the problems of high computation cost and low computation velocity of complex evaluation model,this paper introduces the extreme learning machine as the agent model to replace the complex performance evaluation model. To improve the operational effectiveness of weapons,this paper uses variance-based global sensitivity analysis to find the key factors that affect the effectiveness of weapons,and then find the equipment associated with its function to be improved. This paper uses the typical combat mission of the submarine as the effectiveness optimization case to evaluate the feasibility of the method. Compared with the global sensitivity analysis based on the feed-forward neural network agent model and the support vector regression model,the experimental results verify the validity and efficiency of L-EML model.
引文
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[2]Iooss B,Lemaitre P.A review on global sensitivity analysis methods[M]//Operations Research/Computer Science Interfaces Series(Vol.59).Springer,2014:101-122.
[3]罗鹏程,傅攀峰.武器装备敏感性分析方法综述[J].计算机工程与设计,2008,29(21):5546-5549.
[4]张晓航.防空导弹武器装备体系作战效能全局敏感性分析方法研究[D].长沙:国防科学技术大学,2010.
[5]Nossent J,Elsen P,Bauwens W.Sobol’sensitivity analy-sis of a complex environmental model[J].Environmental Modelling and Software,2011,26(12):1515-1525.
[6]Zhang Chi,Chu Jinggang,Fu Guangtao.Sobol’s sensitivity analysis for a distributed hydrological model of Yichun River Basin,China[J].Journal of Hydrology,2013,480:58-68.
[7]Mathieu A,Vidal T,Jullien A,et al.Sensitivity analysis to help individual plant model parameterization for winter oilseed rape[C]//IEEE International Conference on Functional-Structural Plant Growth Modeling,Simulation,Visualization and Applications.2017:133-139.
[8]Zhan Che-sheng,Song Xiao-meng,Xia Jun,et al.An efficient integrated approach for global sensitivity analysis of hydrological model parameters[J].Environmental Modelling&Software,2013,41(41):39-52.
[9]孔凡哲,宋晓猛,占车生,等.水文模型参数敏感性快速定量评估的RSMSobol方法[J].地理学报,2011,66(9):1270-1280.
[10]Luo Jiannan,Lu Wenxi.Sobol’sensitivity analysis of NAPL-contaminated aquifer remediation process based on multiple surrogates[J].Computers&Geosciences,2014,67:110-116.
[11]Huang Guang-bin,Zhu Qin-yu,Siew C K.Extreme learning machine:A new learning scheme of feedforward neural networks[C]//Proceedings of IEEE International Joint Conference on Neural Networks.2004:985-990.
[12]高尚,娄寿春.武器系统效能评定方法综述[J].系统工程理论与实践,1998,18(7):109-114.
[13]吴晓锋,钱东.用于系统效能分析的WSEIAC模型及其扩展[J].系统工程理论与实践,2000,20(8):1-6.
[14]Ding Shifei,Zhao Han,Zhang Yanan,et al.Extreme learning machine:Algorithm,theory and applications[J].Artificial Intelligence Review,2015,44(1):103-115.
[15]魏昕.基于元模型的全局优化算法研究[D].武汉:华中科技大学,2012.
[16]Mckay M D,Beckman R J,Conover W J.A comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J].Technometrics,2000,42(1):55-61.
[17]Buslenko N P,Golenko D I,Sobol I M,et al.The Monte Carlo method[J].Journal of the American Statistical Association,1949,44(247):335-341.
[18]Sobol I M.On the distribution of points in a cube and the approximate evaluation of integrals[J].USSR Computational Mathematics&Mathematical Physics,1976,7(4):86-112.
[19]周心莲.几个常用随机数及其性质的比较[J].郧阳师范高等专科学校学报,2010,30(6):13-17.
[20]Wang Chen,Duan Qingyun,Gong Wei,et al.An evaluation of adaptive surrogate modeling based optimization with two benchmark problems[J].Environmental Modelling&Software,2014,60(76):167-179.
[21]Sobol I M.Sensitivity estimates for nonlinear mathematical models[J].Matem Mod,1993,2(1):112-118.
[22]Paruggia M.Sensitivity analysis in practice:A guide to assessing scientific models[J].Journal of the Royal Statistical Society,2005,168(2):466.