情报数据驱动的在线仿真系统动态修正方法
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摘要
为了保证面向指挥决策支持的在线仿真系统可信性,提出了情报数据驱动的在线仿真系统动态修正方法。首先,依据情报数据变化快慢,将其分为缓变和快变两类数据;然后,从位置接近性和外形相似性两个方面刻画缓变数据间差异,从趋势项差异和平稳项差异两个方面刻画快变数据间差异,由此实现了仿真输出与情报数据一致性的度量;进一步,基于拉丁超立方实验设计方法确定仿真模型"修正集合",并利用主成分分析综合仿真输出一致性指标,从修正集合中选择使得仿真模型可信性最佳的修正方案。通过应用实例,表明了方法的有效性。
To ensure the creditability of the online simulation system for decision-making,the method for dynamically modifying the online simulation based on intelligence data-driven is proposed.The intelligence data are divided into gradual data and fast data according to the change ratio.For describing the consistency between the simulation output and the intelligence data,the differences between the gradual data are depicted by the proximity of the position and the similarity of the shape,the fast data is decomposed into the trend item and stationary item,and the measure models of differences for each item are given.Furthermore,the modification set of the simulation model is gained via Latin hypercube sampling,the best modification scheme is selected by integrating the consistency indexes of the simulation output based on principal component analysis.Finally,the validity of the method is shown in the application.
To ensure the creditability of the online simulation system for decision-making,the method for dynamically modifying the online simulation based on intelligence data-driven is proposed.The intelligence data are divided into gradual data and fast data according to the change ratio.For describing the consistency between the simulation output and the intelligence data,the differences between the gradual data are depicted by the proximity of the position and the similarity of the shape,the fast data is decomposed into the trend item and stationary item,and the measure models of differences for each item are given.Furthermore,the modification set of the simulation model is gained via Latin hypercube sampling,the best modification scheme is selected by integrating the consistency indexes of the simulation output based on principal component analysis.Finally,the validity of the method is shown in the application.
引文
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[4]Fujimoto R,Lunceford D,Page E,et al.Summary of the parallel/distributed simulation working group[R].Grand Challenges for Modeling and Simulation,2002.
[5]DDDAS Workshop Report.NSF sponsored workshop on DDDAS-dynamic data driven applications systems[EB/OL].[2015-05-30].http:∥www.cise.nsf.gov/dddas,2006-01-20/2007-09-18.
[6]Hu X F,He X Y,Xu X L.The challenge and thinking of modeling and simulation in the era of big data[J].China Science:Information Science,2014,44(5):676-692.(胡晓峰,贺筱媛,徐旭林.大数据时代对建模仿真的挑战与思考[J].中国科学:信息科学,2014,44(5):676-692.)
[7]Naylor T H,Finger J M.Verification of computer simulation models[J].Management Science,1967,14(2):92-101.
[8]Kheir N A,Holmes W M.On validating simulation models of missile systems[J].Simulation,1978,30(4):117-128.
[9]Wu J,Wu X Y,Chen Y X,et al.Validation of simulation models based on improved grey relational analysis[J].Systems Engineering and Electronics,2010,32(8):1677-1679.(吴静,吴晓燕,陈永兴,等.基于改进灰色关联分析的仿真模型验证方法[J].系统工程与电子技术,2010,32(8):1677-1679.)
[10]Damborg M J.An example of error analysis in dynamic model validation[J].Simulation,1985,44(6):301-305.
[11]Liu Z Z.Simulation validation based on the data of the aero experimentation[J].Journal of System Simulation,2002,14(3):281-284.(刘藻珍.基于飞行试验数据的仿真模型验证方法的研究[J].系统仿真学报,2002,14(3):281-284.)
[12]Jiao S,Li W,Yang M.Validation of simulation models based on empirical modal decomposition and grey relevance analysis[J].Systems Engineering and Electronics,2013,35(12):2613-2618.(焦松,李伟,杨明.基于经验模态分解和灰色关联度分析的仿真模型验证方法[J].系统工程与电子技术,2013,35(12):2613-2618.)
[13]Xiong Y,Chen W,Tsui K L,et al.A better understanding ofmodel updating strategies in validating engineering models[J].Computer Methods in Applied Mechanics and Engineering,2009,198(15):1327-1337.
[14]Yuan J,Ng S H,Tsui K L.Calibration of stochastic computer models using stochastic approximation methods[J].IEEE Trans.on Automation Science and Engineering,2013,10(1):171-186.
[15]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J].Proceedings of the Royal Society Mathematical Physical&Engineering Sciences,1998,454:903-995.
[16]Minasny B,McBratney A B.A conditioned Latin hypercube method for sampling in the presence of ancillary information[J].Computers&Geosciences,2006,32(9):1378-1388.
[17]Huo D H,Yang D,Zhang X H,et al.Principal component analysis based Codebook back-ground modeling algorithm[J].Acta Automatica Sinica,2012,38(4):591-600.(霍东海,杨丹,张小洪,等.一种基于主成分分析的Codebook背景建模算法[J].自动化学报,2012,38(4):591-600.)