基于特征差异的仿真模型验证及选择方法

李伟; 焦松; 陆凌云; 杨明

doi:10.3724/SP.J.1004.2014.02134

基于特征差异的仿真模型验证及选择方法

doi: 10.3724/SP.J.1004.2014.02134

1.
哈尔滨工业大学控制与仿真中心哈尔滨 150080

基金项目:

国家自然科学基金 (61273226), 中央高校基本科研业务费专项资金 (HIT.NSRIF.2015035)资助

详细信息

作者简介:
李伟哈尔滨工业大学讲师. 主要研究方向为仿真分析与评估, 分布式仿真.E-mail: feehit@163.com

计量
- 文章访问数: 1539
- HTML全文浏览量: 99
- PDF下载量: 1171
- 被引次数: 0
出版历程
- 收稿日期: 2013-11-13
- 修回日期: 2014-04-18
- 刊出日期: 2014-10-20

Validation and Selection of Simulation Model Based on the Feature Differences

1.
Control and Simulation Center, Harbin Institute of Technology, Harbin 150080

Funds:

Supported by National Natural Science Foundation of China (61273226) and Fundamental Research Funds for the Central Universities (HIT.NSRIF.2015035)

摘要

摘要: 为了实现在系统存在多个数据类型各异的输出时, 多个备选仿真模型的验证和择优, 提出了基于特征差异的仿真模型验证及选择方法. 首先,将系统输出分为静态、缓变和速变三类数据, 并分别给出了每类数据的特征差异度量模型; 然后,采用主成分分析法从多个具有相关性的特征差异中提取出少数几个独立的主成分变量; 再者依据主成分数据, 采用K-均值聚类分析方法将多个备选仿真模型的输出划分为K类; 最后,基于Fisher判别分析法判定参考输出是否属于其中的某类, 进而实现对多个备选仿真模型的验证和选择. 通过实例应用, 表明了该方法的有效性.
- 模型验证 /
- 模型选择 /
- 特征差异 /
- 主成分分析 /
- 聚类分析 /
- 判别分析
Abstract: To validate alternative models and select the most credible one when the models have many outputs of different kinds, a validation and selection method of simulation model based on feature differences is proposed. The outputs are divided into three kinds: static data, gradual data and fast data, and the measure models of feature differences for each kind data are given. The correlation among the feature differences is eliminated via principal component analysis, and several independent principal components are gained. Furthermore, the outputs of simulation models are divided into K kinds of clusters based on K-means clustering according to the principal components. Which cluster the output of actual system belongs to is judged by Fisher discriminant analysis. So the validation and selection of alternative models are realized. Finally, the method is validated in an application.
- Validation of simulation model /
- selection of simulation model /
- feature difference /
- principal component analysis /
- clustering analysis /
- discriminant analysis

HTML全文

参考文献(25)

[1]	Sargent R G. Verification and validation of simulation model. In: Proceeding of the 2010 Winter Simulation Conference. Baltimore, MD: IEEE, 2010. 166-183
[2]	Liu Fei, Ma Ping, Yang Ming, Wang Zi-Cai. Research on credibility quantificaiton of complex simulation systems. Journal of Harbin Institute of Technology, 2007, 39(1): 1-3(刘飞, 马萍, 杨明, 王子才. 复杂仿真系统可信度量化研究. 哈尔滨工业大学学报, 2007, 39(1): 1-3)
[3]	Lu Shao-Wen. Two issues towards verification of simulation of superposed alternative renewal processes. Acta Automatica Sinica, 2009, 35(5): 636-640(卢绍文. 重叠交替更新过程的DTSS仿真校验的两个问题. 自动化学报, 2009, 35(5): 636-640)
[4]	[4] Min Fei-Yan, Yang Ming, Wang Zi-Cai. Knowledge-based method for the validation of complex simulation models. Simulation Modelling Practice and Theory, 2010, 18(5): 500-515
[5]	[5] Naylor T H, Finger J M. Verification of computer simulation models. Management Science, 1967, 14(2): 92-101
[6]	[6] Kheir N A, Holmes W M. On validating simulation models of missile systems. Simulation, 1978, 30(4): 117-128
[7]	Wu Jing, Wu Xiao-Yan, Chen Yong-Xing, Teng Jiang-Chuan. Validation of simulation models based on improved grey relational analysis. Systems Engineering and Electronics, 2010, 32(8): 1677-1679(吴静, 吴晓燕, 陈永兴, 滕江川. 基于改进灰色关联分析的仿真模型验证方法. 系统工程与电子技术, 2010, 32(8): 1677-1679)
[8]	[8] Damborg M J. An example of error analysis in dynamic model validation. Simulation, 1985, 44(6): 301-305
[9]	Liu Shi-Kao, Liu Xing-Tang, Zhang Wen. Fixed quantity evaluation to reliability of simulation system with similar degree. Journal of System Simulation, 2002, 14(2): 143-145(柳世考, 刘兴堂, 张文. 利用相似度对仿真系统可信度进行定量评估. 系统仿真学报, 2002, 14(2): 143-145)
[10]	Montgomery D C, Conard R G. Comparison of simulation and flight-test data for missile systems. Simulation, 1980, 34(2): 63-72
[11]	Liu Zao-Zhen. Simulation validation based on the data of the aero experimentation. 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. 系统仿真学报, 2002, 14(3): 281-284)
[12]	Li Peng-Bo, Gao Xia. Application of MESA on validating missile simulation model. Journal of National University of Defense Technology, 1999, 21(2): 9-12(李鹏波, 高霞. 应用最大熵谱估计进行导弹系统的仿真模型验证. 国防科技大学学报, 1999, 21(2): 9-12)
[13]	Wei Hua-Liang, Liu Zao-Zhen. Cross spectral estimation and its application in missile systems simulation validation. Journal of System Simulation, 1997, 9(3): 116-121(魏华梁, 刘藻珍. 交叉谱估计及其在导弹系统仿真模型验证中的应用. 系统仿真学报, 1997, 9(3): 116-121)
[14]	Balci O. Verification, validation, and cretification of modeling and simulation applications. In: Proceedings of the 2003 Winter Simulation Conference. New Orleans: ACM, 2003: 150-158
[15]	Kleijnen J P C. An overview of the design and analysis of simulation experiments for sensitivity analysis. European Journal of Operational Research, 2005, 164(2): 287-300
[16]	Liu Hui-Ying, Sun Zhen, Liu Xin. Research and implementation of missile simulation model validation system. Modern Electronics Technique, 2012, 35(3): 1-4(刘慧英, 孙真, 刘昕. 导弹仿真模型验证系统的研究与实现. 现代电子技术, 2012, 35(3): 1-4)
[17]	Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q, Yen N C, Tung C C, Liu H H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A, 1998, 454(1971): 903-995
[18]	Ivan M C, Baraniuk R G. Empirical mode decomposition based time-frequency attributes. In: Proceedings of the 69th SEG Meeting. Houston, USA, 1999.
[19]	Huo Dong-Hai, Yang Dan, Zhang Xiao-Hong, Hong Ming-Jian. Principal component analysis based codebook background modeling algorithm. Acta Automatica Sinica, 2012, 38(4): 591-600(霍东海, 杨丹, 张小洪, 洪明坚. 一种基于主成分分析的Codebook背景建模算法. 自动化学报, 2012,38(4): 591-600)
[20]	Pan Chun-Guang, Chen Ying-Wu, Wang Hao. Principal component analysis' application to the software metrics-based for risk assessment. Operations Research and Management Science, 2005, 14(5): 80-84(潘春光, 陈英武, 汪浩. 主成分分析法在基于度量的软件风险评估中的应用. 运筹与管理, 2005, 14(5): 80-84)
[21]	Jain A K. Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 2010, 31(8): 651-666
[22]	Zhang Wen-Jun, Gu Xing-Fa, Chen Liang-Fu, Yu Tao, Xu Hua. An algorithm for initilizing of K-Means clustering based on mean-standard deviation. Journal of Remote Sensing, 2006, 10(5): 715-721(张文君, 顾行发, 陈良富, 余涛, 许华. 基于均值-标准差的K均值初始聚类中心选取算法. 遥感学报, 2006, 10(5): 715-721)
[23]	Maulik U, Bandyopadhyay S. Performance evaluation of some clustering algorithms and validity indices. IEEE Transctions on Pattern Analysis and Machine Intelligence, 2002, 24(12): 1650-1654
[24]	Cheng Zheng-Dong, Zhang Yu-Jin, Fan Xiang, Zhu Bin. Study on discriminant matrics of commonly-used Fisher discriminant functions. Acta Automatica Sinica, 2010, 36(10): 1361-1370(程正东, 章毓晋, 樊祥, 朱斌. 常用Fisher判别函数的判别矩阵研究. 自动化学报, 2010, 36(10): 1361-1370)
[25]	Chiang L H, Kotanchek M E, Kordon A K. Fault diagnosis based on Fisher discriminant analysis and support vector machines. Computers and Chemical Engineering, 2004, 28(8): 1389-1401