1 |
MACKAY N, PRICE C, WOOD A J Weight of shell must tell: a Lanchestrian reappraisal of the battle of jutland. History, 2016, 101 (347): 536- 563.
doi: 10.1111/1468-229X.12241
|
2 |
JOHNSON I R, MACKAY N J Lanchester models and the battle of Britain. Naval Research Logistics, 2011, 58 (3): 210- 222.
doi: 10.1002/nav.20328
|
3 |
KEANE T Combat modelling with partial differential equations. Applied Mathematical Modelling, 2011, 35 (6): 2723- 2735.
doi: 10.1016/j.apm.2010.11.057
|
4 |
KIM D, MOON H, PARK D, et al An efficient approximate solution for stochastic Lanchester models. Journal of the Operational Research Society, 2017, 68 (11): 1470- 1481.
doi: 10.1057/s41274-016-0163-6
|
5 |
KRESS M, CAULKINS J P, FEICHTINGER G, et al Lanchester model for three-way combat. European Journal of Operational Research, 2018, 264 (1): 46- 54.
doi: 10.1016/j.ejor.2017.07.026
|
6 |
CAO H H, TIAN S P, ZHANG Y Q Lanchester combat model under psychological factor. Fire Control & Command Control, 2018, 43 (3): 58- 62.
|
7 |
DEITCHMAN S A Lanchester model of guerrilla war. Operations Research, 1962, 10 (6): 818- 827.
doi: 10.1287/opre.10.6.818
|
8 |
TANG Q C, LIU G F Information warfare model improvement based on Lanchester’s equation. Journal of Command and Control, 2017, 3 (3): 201- 207.
|
9 |
KRESS M Lanchester models for irregular warfare. Mathematics, 2020, 8 (5): 737.
doi: 10.3390/math8050737
|
10 |
JOHNSON D D P, MACKAY N J Fight the power: Lanches-ter’s laws of combat in human evolution. Evolution and Human Behavior, 2015, 36 (2): 152- 163.
doi: 10.1016/j.evolhumbehav.2014.11.001
|
11 |
SONG H Q, ZHAO D M, YUAN C Y Network security situation prediction of improved Lanchester equation based on time action factor. Mobile Networks & Applications, 2021, 26 (3): 1008- 1023.
|
12 |
KALLONIATIS A C, HOEK K, ZUPARIC M, et al Optimising structure in a networked Lanchester model for fires and manoeuvre in warfare. Journal of the Operational Research Society, 2021, 72 (8): 1863- 1878.
doi: 10.1080/01605682.2020.1745701
|
13 |
MCCARTNEY M The solution of Lanchester’s equations with inter-battle reinforcement strategies. Physica A-Statistical Mechanics and Its Applications, 2022, 586, 126477.
doi: 10.1016/j.physa.2021.126477
|
14 |
ATKINSON M P, KRESS M, MACKAY N J Targeting, deployment, and loss-tolerance in Lanchester engagements. Operations Research, 2021, 69 (1): 71- 81.
doi: 10.1287/opre.2020.2022
|
15 |
COULSON S G Lanchester modelling of intelligence in combat. IMA Journal of Management Mathematics, 2019, 30 (2): 149- 164.
doi: 10.1093/imaman/dpx014
|
16 |
CLARK B A, WALTON T. Taking back the seas: transforming the U.S. surface fleet for decision-centric warfare.https://csbaonline.org/research/publications/taking-back-the-seas-transforming-the-u.s-surface-fleet-for-decision-centric-warfare/publication/1.
|
17 |
TORRA V Hesitant fuzzy sets. International Journal of Intelligent Systems, 2010, 25 (6): 529- 539.
|
18 |
FARHADINIA B Information measures for hesitant fuzzy sets and interval-valued hesitant fuzzy sets. Information Sciences, 2013, 240, 129- 144.
doi: 10.1016/j.ins.2013.03.034
|
19 |
ZADEH L A The concept of a linguistic variable and its application to approximate reasoning−II. Information Sciences, 1975, 8 (4): 301- 357.
|
20 |
LIANG D C, XU Z S The new extension of TOPSIS method for multiple criteria decision making with hesitant Pythagorean fuzzy sets. Applied Soft Computing, 2017, 60, 167- 179.
doi: 10.1016/j.asoc.2017.06.034
|
21 |
RODRIGUEZ R M, MARTINEZ L, HERRERA F Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. on Fuzzy Systems, 2012, 20 (1): 109- 119.
doi: 10.1109/TFUZZ.2011.2170076
|
22 |
LIAO H C, XU Z S, ZENG X J Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Information Sciences, 2014, 271, 125- 142.
doi: 10.1016/j.ins.2014.02.125
|
23 |
WU Z B, XU J P A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters. Information Fusion, 2018, 41, 217- 231.
doi: 10.1016/j.inffus.2017.09.011
|
24 |
XIA M M, XU Z S Hesitant fuzzy information aggregation in decision making. International Journal of Approximate Reasoning, 2011, 52 (3): 395- 407.
doi: 10.1016/j.ijar.2010.09.002
|
25 |
LUO Y Y, LI H, WANG R F, et al The characteristics and key technologies of the common domain in the cognitive domain. Defense Technology Review, 2018, 39 (4): 58- 62.
|
26 |
WEI C P, RODRIGUEZ R M, MARTINEZ L Uncertainty measures of extended hesitant fuzzy linguistic term sets. IEEE Trans. on Fuzzy Systems, 2018, 26 (3): 1763- 1768.
doi: 10.1109/TFUZZ.2017.2724023
|
27 |
HU J H, YANG Y, ZHANG X L, et al Similarity and entropy measures for hesitant fuzzy sets. International Transactions in Operational Research, 2018, 25 (3): 857- 886.
doi: 10.1111/itor.12477
|
28 |
LIAO H C, XU Z S A VIKOR-based method for hesitant fuzzy multi-criteria decision making. Fuzzy Optimization and Decision Making, 2013, 12 (4): 373- 392.
doi: 10.1007/s10700-013-9162-0
|
29 |
FARHADINIA B Multiple criteria decision-making methods with completely unknown weights in hesitant fuzzy linguistic term setting. Knowledge-Based Systems, 2016, 93, 135- 144.
doi: 10.1016/j.knosys.2015.11.008
|
30 |
HAN Q, LI W, LU Y L, et al TOPSIS method based on novel entropy and distance measure for linguistic pythagorean fuzzy sets with their application in multiple attribute decision making. IEEE Access, 2020, 8, 14401- 14412.
doi: 10.1109/ACCESS.2019.2963261
|
31 |
HAN Q, LI Z W, SONG Y F, et al A new method for MAGDM based on improved TOPSIS and a novel Pythagorean fuzzy soft entropy. Symmetry-Basel, 2019, 11 (7): 905.
doi: 10.3390/sym11070905
|