1 |
罗鹏程, 傅攀峰, 周经伦. 武器装备体系作战能力评估框架[J]. 系统工程与电子技术, 2005, 27 (1): 72- 75.
|
|
LUO P C , FU P F , ZHOU J L . Combat capability evaluation framework of weapon equipment system[J]. Systems Engineering and Electronics, 2005, 27 (1): 72- 75.
|
2 |
杨克巍, 杨志伟, 谭跃进, 等. 面向体系贡献率的装备体系评估方法研究综述[J]. 系统工程与电子技术, 2019, 41 (2): 311- 321.
|
|
YANG K W , YANG Z W , TAN Y J , et al. Review of the evaluation methods of equipment system of systems facing the contribution rate[J]. Systems Engineering and Electronics, 2019, 41 (2): 311- 321.
|
3 |
舒宇.基于能力需求的武器装备体系结构建模方法与应用研究[D].长沙:国防科技大学, 2009.
|
|
SHU Y. Research on modeling method and application of weapon equipment architecture based on capability requirement[D]. Changsha: National University of Defense Technology, 2009.
|
4 |
程贲, 谭跃进, 黄魏, 等. 基于能力需求视角的武器装备体系评估[J]. 系统工程与电子技术, 2011, 33 (2): 320- 323.
|
|
CHENG B , TAN Y J , HUANG W , et al. Evaluation of weapon equipment system from the perspective of capability requirements[J]. Systems Engineering and Electronics, 2011, 33 (2): 320- 323.
|
5 |
朱子薇, 侯磊, 程思齐, 等. 武器装备效能评估指标体系分析与研究[J]. 火力与指挥控制, 2016, 41 (11): 197- 200.
|
|
ZHU Z W , HOU L , CHENG S Q , et al. Analysis and research on weapon equipment effectiveness evaluation index system[J]. Firepower and Command Control, 2016, 41 (11): 197- 200.
|
6 |
赵青松, 鲁延京, 李善飞. 面向使命任务的武器装备体系能力关联分析[J]. 火力与指挥控制, 2011, 36 (3): 24- 27, 31.
|
|
ZHAO Q S , LU Y J , LI S F . Capability correlation analysis of weapon equipment system oriented to mission[J]. Firepower and Command Control, 2011, 36 (3): 24- 27, 31.
|
7 |
LING C , WANG K , LIU Z X , et al. Research on weapon system combat test scheme design[J]. Journal of the Academy of Equipment, 2016, 27 (6): 112- 116.
|
8 |
XUE Y Z , ZHOU F . Method for establishing weapon equipment battle test evaluation index system[J]. Journal of the Academy of Equipment, 2016, 27 (4): 102- 107.
|
9 |
罗小明, 杨娟, 朱延雷. 新型武器装备作战试验评估体系构建[J]. 军械工程学院学报, 2016, 28 (4): 1- 7.
|
|
LUO X M , YANG J , ZHU Y L . Building of evaluation system for operational testing of new types of weapons and equipment[J]. Journal of Ordnance Engineering College, 2016, 28 (4): 1- 7.
|
10 |
ZAKERZADEH M R , GERDEFARAMARZI M S , BOZORG M . Parameter estimation of an SMA actuator model using an extended Kalman filter[J]. Mechatronics, 2018, 50 (4): 148- 159.
|
11 |
FERRARI A , GINIS P , HARDEGGER M , et al. A mobile Kalman-filter based solution for the real-time estimation of spatio-temporal gait parameters[J]. IEEE Trans.on Neural Systems and Rehabilitation Engineering, 2016, 24 (7): 764- 733.
|
12 |
MONTAZERI A , WEST C , MONK S D , et al. Dynamic modeling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm[J]. International Journal of Control, 2016, 90 (4): 661- 683.
|
13 |
LU Y Z , YAN D P , LEVY D . Parameter estimation of vertical take off and landing aircrafts by using a PID controlling particle swarm optimization algorithm[J]. Applied Intelligence, 2015, 44 (4): 793- 815.
|
14 |
OTTONI A L C , NEPOMUCENO E G , OLIVEIRA M S D . A response surface model approach to parameter estimation of reinforcement learning for the travelling salesman problem[J]. Journal of Control, Automation and Electrical Systems, 2018, 29, 350- 359.
|
15 |
QUEIPO N V , HAFTKA R T , SHYY W , et al. Surrogate-based analysis and optimization[J]. Progress in Aerospace Sciences, 2005, 41 (1): 1- 28.
|
16 |
THUONG N N P , THIEN H T , HONG N L T , et al. Application of response surface methodology to optimize the process of saponification reaction from coconut oil in ben tre-vietnam[J]. Solid State Phenomena, 2018, 279, 235- 239.
|
17 |
MILOVANOVIC S , VON S L . Radial basis function generated finite differences for option pricing problems[J]. Computers & Mathematics with Applications, 2018, 75 (4): 1462- 1481.
|
18 |
GUHANIYOGI R , BANERJEE S . Meta-Kriging: scalable Bayesian modeling and inference for massive spatial datasets[J]. Technometrics, 2018, 60 (4): 430- 444.
|
19 |
ALANIS A Y . Electricity prices forecasting using artificial neural networks[J]. IEEE Latin America Transactions, 2018, 16 (1): 105- 111.
|
20 |
HEDDAM S , KISI O . Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree[J]. Journal of Hydrology, 2018, 559, 499- 509.
|
21 |
KOZIEL S , CHENG Q S , BANDLER J W . Space mapping[J]. IEEE Microwave Magazine, 2008, 9 (6): 105- 122.
|
22 |
STOCKWELL D R B , PETERSON A T . Effects of sample size on accuracy of species distribution models[J]. Ecological Modelling, 2002, 148 (1): 1- 13.
|
23 |
MC K M D , BECHMAN 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]. Techno-metrics, 1979, 21 (2): 239- 245.
|
24 |
HAMDIA K M , SILANI M , ZHUANG X , et al. Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions[J]. International Journal of Fracture, 2017, 206 (2): 215- 227.
|
25 |
QIAN Z , SEEPERSAD C C , JOSEPH V R , et al. Building surrogate models based on detailed and approximate simulations[J]. Journal of Mechanical Design, 2006, 128 (4): 668- 677.
|
26 |
SIMPSON T W, TOROPOV V, BALABANOV V, et al. Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come-or not[C]//Proc.of the 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2008.
|
27 |
HAN Z H , GÖRTZ S , ZIMMERMANN R . Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function[J]. Aerospace Science and Technology, 2013, 25 (1): 177- 189.
|
28 |
MUKHOPADHYAY T , CHAKRABORTY S , DEY S , et al. A critical assessment of kriging model variants for high-fidelity uncertainty quantification in dynamics of composite shells[J]. Archives of Computational Methods in Engineering, 2017, 24 (3): 495- 518.
|
29 |
BILIC D, BROSSE E, SADOVYKH A, et al. An integrated model-based tool chain for managing variability in complex system design[C]//Proc.of the IEEE Models & Evolution Workshop, 2019.
|
30 |
ZHAO L , CHOI K K , LEE I . Metamodeling method using dynamic kriging for design optimization[J]. AIAA Journal, 2011, 49 (9): 2034- 2046.
|
31 |
CHEN X , REN H , BI L C , et al. Aircraft complex system diagnosis based on design knowledge and real-time monitoring information[J]. Journal of Aerospace Information Systems, 2018, 15 (7): 414- 426.
|
32 |
ZHAN Y , LUO Y Z , DENG X F , et al. Satellite-based estimates of daily No.2 exposure in China using hybrid random forest and spatiotemporal Kriging model[J]. Environmental Science & Technology, 2018, 52 (7): 4180- 4189.
|
33 |
BHOSEKAR A , IERAPETRITOU M . Advances in surrogate based modeling, feasibility analysis, and optimization: a review[J]. Computers & Chemical Engineering, 2018, 108 (4): 250- 267.
|
34 |
YANG C G , JIANG Y M , HE W , et al. Adaptive parameter estimation and control design for robot manipulators with finite-time convergence[J]. IEEE Trans.on Industrial Electronics, 2018, 65 (10): 8112- 8123.
|
35 |
LUAN F, NA J, YANG J, et al. Robust adaptive finite-time parameter estimation for linearly parameterized nonlinear systems[C]//Proc.of the Control Conference, 2013.
|
36 |
HAGHIGHAT A K , ROUMI S , MADANI N , et al. An intelligent cooling system and control model for improved engine thermal management[J]. Applied Thermal Engineering, 2018, 128, 253- 263.
|
37 |
XI W U , GE M U , LI D Y . Research on design principles of weapon equipment operational test[J]. Journal of Ordnance Equipment Engineering, 2019, 40 (1): 24- 27.
|
38 |
NATHANIEL V D E , JESTER D , ROY D , et al. Test anxiety effects, predictors, and correlates: a 30-year meta-analytic review[J]. Journal of Affective Disorders, 2018, 277 (2): 483- 493.
|
39 |
HAUSE M . An MBSE eco-system for performing trade-off analysis[J]. Insight, 2018, 21 (4): 29- 31.
|
40 |
CEN R M, LIU L, ZHENG L G. Research on the method of the test and evaluation of complex simulation system[C]//Proc.of the International Symposium on Computers & Informatics, 2015.
|