Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (5): 1161-1172.doi: 10.23919/JSEE.2022.000112
-
收稿日期:2021-11-15接受日期:2022-06-13出版日期:2022-10-27发布日期:2022-10-27
Operational effectiveness evaluation based on the reduced conjunctive belief rule base
Ziwei ZHANG1,2(
), Qisheng GUO1,*(), Zhiming DONG1(), Hongxiang LIU3(), Ang GAO1(), Pengcheng QI1()
- 1 Military Exercise and Training Center, Army Academy of Armored Forces, Beijing 100072, China
2 Unit 32128 of the PLA, Ji’nan 250024, China
3 Department of Equipment Support and Remanufacture, Army Academy of Armored Forces, Beijing 100072, China
-
Received:2021-11-15Accepted:2022-06-13Online:2022-10-27Published:2022-10-27 -
Contact:Qisheng GUO E-mail:41872293@qq.com;Qisheng.Guo@163.com;dong_zhiming@163.com;13811816672@139.com;15689783388@163.com;13573129023@139.com -
About author:|ZHANG Ziwei was born in 1986. He received his M.S. degree in Shandong University. He is pursuing his Ph.D. degree in Army Academy of Arm-ored Forces. His research interests are equipment tests and operational effectiveness evaluation. E-mail:
41872293@qq.com ||GUO Qisheng was born in 1962. He received his Ph.D. degree from Tsinghua University. He is currently a professor in Army Academy of Armored Forces. His research interests are equipment requirement demonstration and equipment tests. E-mail:
Qisheng.Guo@163.com ||DONG Zhiming was born in 1977. He received his Ph.D. degree from Army Academy of Armored Forces. He is currently a professor in Army Academy of Armored Forces. His research interests are equipment requirement demonstration and equipment tests. E-mail:
dong_zhiming@163.com ||LIU Hongxiang was born in 1987. He received his M.S. degree from Army Academy of Armored Forces. He is currently an associate professor in Army Academy of Armored Forces. His research interest is equipment support technology. E-mail:
13811816672@139.com ||GAO Ang was born in 1988. He received his M.S. degree in Army Academy of Armored Forces. He is pursuing his Ph.D. degree in Army Academy of Armored Forces. His research interest is the intelligent decision of computer-generated force based on multi-agent deep reinforcement learning. E-mail:
15689783388@163.com ||QI Pengcheng was born in 1990. He received his B.S. degree in electrical engineering and automation in Army Academy of Armored Forces. He is a M.S. candidate in Army Academy of Armored Forces. His research interest is operational effectiveness evaluation. E-mail:
13573129023@139.com -
Supported by:This work was supported by the Military Scientific Research Program (41401020301).
引用本文
. [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1161-1172.
Ziwei ZHANG, Qisheng GUO, Zhiming DONG, Hongxiang LIU, Ang GAO, Pengcheng QI. Operational effectiveness evaluation based on the reduced conjunctive belief rule base[J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1161-1172.
"
"
"
| Serial number | Rule premise | Rule conclusion |
| 1 | | |
| 2 | | |
| 3 | | |
| 4 | | |
| 5 | | |
| 6 | | |
| 7 | | |
| 8 | | |
| 9 | | |
| 10 | | |
| 11 | | |
| 12 | | |
| 13 | | |
| 14 | | |
| 15 | | |
| 16 | | |
| 17 | | |
| 18 | | |
| 19 | | |
| 20 | | |
| 21 | | |
| 22 | | |
| 23 | | |
| 24 | | |
| 25 | | |
| 26 | | |
| 27 | | |
"
| Serial number | Rule premise | Rule conclusion |
| 1 | | |
| 2 | | |
| 3 | | |
"
"
| Line number | Factor | |||
| a | b | c | d | |
| 1 | 0 | 0 | 0+0=0 | 0×2+0=0 |
| 2 | 0 | 1 | 0+1=1 | 0×2+1=1 |
| 3 | 0 | 2 | 0+2=2 | 0×2+2=2 |
| 4 | 1 | 0 | 1+0=1 | 1×2+0=2 |
| 5 | 1 | 1 | 1+1=2 | 1×2+1=0 |
| 6 | 1 | 2 | 1+2=0 | 1×2+2=1 |
| 7 | 2 | 0 | 2+0=2 | 2×2+0=1 |
| 8 | 2 | 1 | 2+1=0 | 2×2+1=2 |
| 9 | 2 | 2 | 2+2=1 | 2×2+2=0 |
| Listing | a | b | a+b | a×2+b |
"
| Serial number | Rule premise | Rule conclusion |
| 1 | | |
| 5 | | |
| 9 | | |
| 11 | | |
| 15 | | |
| 16 | | |
| 21 | | |
| 22 | | |
| 26 | | |
"
"
| Serial number | Rule item: (c31= | Rule conclusion: c3= ( |
| 1 | c31=0, c32=0, c33=0 | c3= (1,0,0) |
| 2 | c31=0, c32=0, c33=0.5 | c3= (0.9,0.1,0) |
| 3 | c31=0, c32=0, c33=1 | c3= (0.8,0.2,0) |
| 4 | c31=0, c32=0.5, c33=0 | c3= (0.2,0.8,0) |
| 5 | c31=0, c32=0.5, c33=0.5 | c3= (0.2,0.8,0) |
| 6 | c31=0, c32=0.5, c33=1 | c3= (0.2,0.8,0) |
| 7 | c31=0, c32=1, c33=0 | c3= (0,0.4,0.6) |
| 8 | c31=0, c32=1, c33=0.5 | c3= (0,0.4,0.6) |
| 9 | c31=0, c32=1, c33=1 | c3= (0,0.4,0.6) |
| 10 | c31=0.5, c32=0, c33=0 | c3= (0,0.3,0.7) |
| 11 | c31=0.5, c32=0, c33=0.5 | c3= (0,0.3,0.7) |
| 12 | c31=0.5, c32=0, c33=1 | c3= (0,0.3,0.7) |
| 13 | c31=0.5, c32=0.5, c33=0 | c3= (0,0.2,0.8) |
| 14 | c31=0.5, c32=0.5, c33=0.5 | c3= (0,0.2,0.8) |
| 15 | c31=0.5, c32=0.5, c33=1 | c3= (0,0.2,0.8) |
| 16 | c31=0.5, c32=1, c33=0 | c3= (0,0.1,0.9) |
| 17 | c31=0.5, c32=1, c33=0.5 | c3= (0,0.1,0.9) |
| 18 | c31=0.5, c32=1, c33=1 | c3= (0,0.1,0.9) |
| 19 | c31=1, c32=0, c33=0 | c3= (0,0,1) |
| 20 | c31=1, c32=0, c33=0.5 | c3= (0,0,1) |
| 21 | c31=1, c32=0, c33=1 | c3= (0,0,1) |
| 22 | c31=1, c32=0.5, c33=0 | c3= (0,0,1) |
| 23 | c31=1, c32=0.5, c33=0.5 | c3= (0,0,1) |
| 24 | c31=1, c32=0.5, c33=1 | c3= (0,0,1) |
| 25 | c31=1, c32=1, c33=0 | c3= (0,0,1) |
| 26 | c31=1, c32=1, c33=0.5 | c3= (0,0,1) |
| 27 | c31=1, c32=1, c33=1 | c3= (0,0,1) |
"
| Serial number | Evaluation input: (c31,c32,c33) | Evaluation output: (light, medium, heavy) | Effectiveness: E |
| 1 | (0.1,0.6,0.4) | (0.099 9,0.624 9,0.275 2) | 0.412 3 |
| 2 | (0.6,0.5,0.9) | (0.000 0,0.130 5,0.869 5) | 0.065 3 |
| 3 | (0.2,0.1,0.3) | (0.447 0,0.276 0,0.277 0) | 0.585 0 |
| 4 | (0.9,0.8,0.9) | (0.000 0,0.015 8,0.984 2) | 0.007 9 |
| 5 | (0.5,0.4,0.5) | (0.000 0,0.195 5,0.804 5) | 0.097 8 |
| 6 | (0.8,0.9,0.7) | (0.000 0,0.032 7,0.967 3) | 0.016 4 |
| 7 | (0.4,0.3,0.7) | (0.070 7,0.269 4,0.659 8) | 0.205 4 |
| 8 | (0.3,0.2,0.6) | (0.201 1,0.325 2,0.473 7) | 0.363 7 |
| 9 | (0.7,0.7,0.8) | (0.000 0,0.064 6,0.935 4) | 0.032 3 |
"
| Serial number | Rule item: (c31= | Rule conclusion: c3= ( |
| 1 | c31=0, c32=0, c33=0 | c3= (1,0,0) |
| 2 | c31=0, c32=0.5, c33=0.5 | c3= (0.2,0.8,0) |
| 3 | c31=0, c32=1, c33=1 | c3= (0,0.4,0.6) |
| 4 | c31=0.5, c32=0, c33=0.5 | c3= (0,0.3,0.7) |
| 5 | c31=0.5, c32=0.5, c33=1 | c3= (0,0.2,0.8) |
| 6 | c31=0.5, c32=1, c33=0 | c3= (0,0.1,0.9) |
| 7 | c31=1, c32=0, c33=1 | c3= (0,0,1) |
| 8 | c31=1, c32=0.5, c33=0 | c3= (0,0,1) |
| 9 | c31=1, c32=1, c33=0.5 | c3= (0,0,1) |
"
| Serial number | Rule item: (c31= | Rule conclusion: c3= ( |
| 1 | c31=0, c32=0, c33=0 | c3= (0.403 6, 0.3128, 0.2836) |
| 2 | c31=0.5, c32=0.5, c33=0.5 | c3= (0.005 4, 0.1785, 0.8161) |
| 3 | c31=1, c32=1, c33=1 | c3= (0.000 0, 0.0264, 0.9736) |
"
| Serial number | Evaluation input: (c31,c32,c33) | Evaluation output: (light, medium, heavy) | Effectiveness: E′ |
| 1 | (0.1,0.6,0.4) | (0.123 5,0.199 5,0.677 0) | 0.223 3 |
| 2 | (0.6,0.5,0.9) | (0.003 4,0.118 6,0.878 0) | 0.062 8 |
| 3 | (0.2,0.1,0.3) | (0.229 3,0.255 7,0.515 0) | 0.357 2 |
| 4 | (0.9,0.8,0.9) | (0.001 1,0.052 7,0.946 2) | 0.027 5 |
| 5 | (0.5,0.4,0.5) | (0.014 4,0.182 1,0.803 5) | 0.105 4 |
| 6 | (0.8,0.9,0.7) | (0.001 4,0.060 3,0.938 3) | 0.031 6 |
| 7 | (0.4,0.3,0.7) | (0.044 8,0.152 5,0.802 7) | 0.121 1 |
| 8 | (0.3,0.2,0.6) | (0.078 1,0.167 9,0.753 9) | 0.162 1 |
| 9 | (0.7,0.7,0.8) | (0.002 2,0.082 4,0.915 4) | 0.043 4 |
"
"
| Serial number | Evaluation input: (c31,c32,c33) | Reasoning evaluation of the complete conjunctive BRB | Reasoning evaluation of the reduced conjunctive BRB | |||
| Effectiveness: E | Difference ratio: D | Effectiveness: E′ | Difference ratio: D′ | |||
| 3 | (0.2,0.1,0.3) | 0.585 0 | 0.295 2 | 0.357 2 | 0.374 9 | |
| 1 | (0.1,0.6,0.4) | 0.412 3 | 0.117 9 | 0.223 3 | 0.274 0 | |
| 8 | (0.3,0.2,0.6) | 0.363 7 | 0.435 2 | 0.162 1 | 0.253 1 | |
| 7 | (0.4,0.3,0.7) | 0.205 4 | 0.524 1 | 0.121 1 | 0.129 2 | |
| 5 | (0.5,0.4,0.5) | 0.097 8 | 0.332 3 | 0.105 4 | 0.404 7 | |
| 2 | (0.6,0.5,0.9) | 0.065 3 | 0.504 9 | 0.062 8 | 0.308 8 | |
| 9 | (0.7,0.7,0.8) | 0.032 3 | 0.493 9 | 0.043 4 | 0.272 4 | |
| 6 | (0.8,0.9,0.7) | 0.016 4 | 0.516 8 | 0.031 6 | 0.129 7 | |
| 4 | (0.9,0.8,0.9) | 0.007 9 | ? | 0.027 5 | ? | |
| Discrimination | 0.402 5 | ? | 0.268 3 | ? | ||
| 1 | MIAO J M, SHEN B Z, JIA G W, et al An evaluation method of system contribution rate based on task completion degree. Proc. of the 8th China Command and Control Conference, 2020, 589- 593. |
| 2 | WANG H, YANG Y L, ZHANG Q B, et al Operational efficiency evaluation of the air assault brigade based on fuzzy comprehensive evaluation. Journal of Information Engineering University, 2021, 22 (3): 369- 373. |
| 3 | QI Z F, WANG G S Effectiveness evaluation of electronic warfare command and control system based on grey AHP method. Journal of Chemical and Pharmaceutical Research, 2014, 6 (7): 535- 542. |
| 4 |
FENG Z C, HE W, ZHOU Z J, et al A new safety assessment method based on belief rule base with attribute reliability. IEEE/CAA Journal of Automatica Sinica, 2021, 8 (11): 1774- 1785.
doi: 10.1109/JAS.2020.1003399 |
| 5 | ZHOU Z J, CAO Y, HU G Y, et al New health-state assessment model based on belief rule base with interpretability. Science China Information Sciences, 2021, 64 (7): 209- 223. |
| 6 | YANG J B, XU D L Nonlinear information aggregation via evidential reasoning in multi-attribute decision analysis under uncertainty. IEEE Trans. on Systems, Man & Cybernetics, 2002, 32 (3): 372- 393. |
| 7 |
YANG J B, LIU J, WANG J, et al Belief rule-base inference methodology using the evidential reasoning approach-RIMER. IEEE Trans. on Systems, Man & Cybernetics-Part A: Systems & Humans, 2006, 36 (2): 266- 285.
doi: 10.1109/TSMCA.2005.851270 |
| 8 |
CHANG L L, SUN J B, JIANG J, et al Parameter learning for the belief rule base system in the residual life probability prediction of metalized film capacitor. Knowledge-Based Systems, 2015, 73 (1): 69- 80.
doi: 10.1016/j.knosys.2014.09.006 |
| 9 | LIU J D, HAO J G, HUANG J Equipment battle damage assessment based on belief rule base reasoning. Computer Systems & Applications, 2022, 31 (4): 213- 220. |
| 10 | CHANG L L, ZHOU Z J, CHEN Y W, et al Belief rule base structure and parameter joint optimization under disjunctive assumption for nonlinear complex system modeling. IEEE Trans. on Systems, Man & Cybernetics: Systems, 2018, 48 (9): 1542- 1554. |
| 11 |
CHANG L L, ZHOU Z J Akaike information criterion-based conjunctive belief rule base learning for complex system modeling. Knowledge-Based Systems, 2018, 161 (12): 47- 64.
doi: 10.1016/j.knosys.2018.07.029 |
| 12 |
CHANG L L, ZHOU Y, JIANG J, et al Structure learning for belief rule base expert system: a comparative study. Knowledge-Based Systems, 2013, 39 (2): 159- 172.
doi: 10.1016/j.knosys.2012.10.016 |
| 13 | WANG Y M, YANG L H, FU Y G, et al Dynamic rule adjustment approach for optimization belief rule-base expert system. Knowledge-Based Systems, 2016, 9 (3): 40- 60. |
| 14 |
LI G L, ZHOU Z J, HU C C, et al A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base. Safety Science, 2017, 93 (3): 108- 120.
doi: 10.1016/j.ssci.2016.11.011 |
| 15 | ZHOU Z J, CAO Y, HU G Y, et al. New health-state assessment model based on belief rule base with interpretability. Science China Information Sciences, 2021, 64: 172214. |
| 16 | CHANG L L, ZHANG L M, FU C, et al. Transparent digital twin for output control using belief rule base. IEEE Trans. on Cybernetics, 2021. DOI: 10.1109/TCYB.2021.3063285. |
| 17 |
CHENG X Y, LIU S S, HE W, et al A model for flywheel fault diagnosis based on fuzzy fault tree analysis and belief rule base. Machines, 2022, 10 (2): 73.
doi: 10.3390/machines10020073 |
| 18 | FU Y G, YIN Z F, SU M N, et al. Construction and reasoning approach of belief rule-base for classification base on decision tree. IEEE Access, 2020, 8: 138046–138057. |
| 19 | YANG L H, LIU J, YE F F, et al. Highly explainable cumulative belief rule-based system with effective rule-base modeling and inference scheme. Knowledge-Based Systems, 2022, 240(3): 107805. |
| 20 |
CAO Y, ZHOU Z J, HU C H, et al A new approximate belief rule base expert system for complex system modelling. Decision Support Systems, 2021, 150 (11): 113558.
doi: 10.1016/j.dss.2021.113558 |
| 21 |
FENG Z C, ZHOU Z J, HU C H, et al A new belief rule base model with attribute reliability. IEEE Trans. on Fuzzy Systems, 2019, 27 (5): 903- 916.
doi: 10.1109/TFUZZ.2018.2878196 |
| 22 |
WANG J C, WU C F J Nearly orthogonal arrays with mixed levels and small runs. Technometrics, 1992, 34 (4): 409- 422.
doi: 10.1080/00401706.1992.10484952 |
| 23 |
MA C X, FANG K T, LISKI E A new approach in orthogonal and nearly orthogonal arrays. Metrika, 2000, 50, 255- 268.
doi: 10.1007/s001840050049 |
| 24 |
XU H Q An algorithm for constructing orthogonal and nearly-orthogonal arrays with mixed levels and small runs. Technometrics, 2002, 44 (4): 356- 368.
doi: 10.1198/004017002188618554 |
| 25 | ARORA T, SONI R. A pre-screening approach for COVID-19 testing based on belief rule-based expert system. Lecture Notes on Data Engineering and Communications Technologies, 2020, 60(12): 19−28. |
| 26 | YANG Y, FU C, CHEN Y W, et al A belief rule based expert system for predicting consumer preference in new product development. Knowledge-Based Systems, 2016, 94 (2): 105- 113. |
| 27 |
YANG L H, WANG Y M, FU Y G A consistency analysis-based rule activation method for extended belief-rule-based systems. Information Sciences, 2018, 445/446 (6): 50- 65.
doi: 10.1016/j.ins.2018.02.059 |
| 28 | ZHU W, HOU P Z, CHANG L L, et al Disjunctive belief rule base optimization by ant colony optimization for railway transportation safety assessment. Proc. of the 31st China Control and Decision Making Conference, 2019, 822- 826. |
| 29 | WANG X Y, SUN J B, YOU Y Q, et al A new approach for disjunctive belief rule base construction with incomplete conjunctive belief rule base. Proc. of the 31st China Control and Decision Making Conference, 2019, 397- 402. |
| 30 | ZHANG Z W, LI L, DONG Z M, et al. Research on the construction method of simulation evaluation index of operation effectiveness of operation concept traction. Modern Education Management, 2022, 34(3): 603−613. (in Chinese) |
| No related articles found! |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||