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Effectiveness evaluation of fighter using fuzzy Bayes risk weighting method

Published online by Cambridge University Press:  04 June 2018

M. Suo ,
S. Li ,
Y. Chen ,
Z. Zhang ,
B. Zhu  and
R. An
M. Suo*
Affiliation:
School of AstronauticsHarbin Institute of TechnologyHarbinChina
S. Li*
Affiliation:
School of AstronauticsHarbin Institute of TechnologyHarbinChina
Y. Chen
Affiliation:
School of AstronauticsHarbin Institute of TechnologyHarbinChina
Z. Zhang
Affiliation:
School of AstronauticsHarbin Institute of TechnologyHarbinChina
B. Zhu
Affiliation:
School of AstronauticsHarbin Institute of TechnologyHarbinChina
R. An
Affiliation:
School of AstronauticsHarbin Institute of TechnologyHarbinChina
*
buaasuozi@hotmail.com
lishunli@hit.edu.cn
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Abstract

Multiple Attribute Decision Analysis (MADA), known to be simple and convenient, is one of the most commonly used methods for Effectiveness Evaluation of Fighter (EEF), in which the attribute weight assignment plays a key role. Generally, there are two parts in the index system of MADA, i.e. performance index and decision index (or label), which denote the specific performance and the category of the object, respectively. In some index systems of EEF, the labels can be easily obtained, which are presented as the generations of fighters. However, the existing methods of attribute weight determination usually ignore or do not take full advantage of the supervisory function of labels. To make up for this deficiency, this paper develops an objective method based on fuzzy Bayes risk. In this method, a loss function model based on Gaussian kernel function is proposed to cope with the drawback that the loss function in Bayes risk is usually determined by experts. In order to evaluate the credibility of assigned weights, a longitudinal deviation and transverse residual correlation coefficient model is designed. Finally, a number of experiments, including the comparison experiments on University of California Irvine (UCI) data and EEF, are carried out to illustrate the superiority and applicability of the proposed method.

Keywords

Effectiveness evaluationBayes riskWeight assignmentFuzzyGaussian kernelCorrelation coefficientFighter
Type
Research Article
Information
The Aeronautical Journal , Volume 122 , Issue 1254 , August 2018 , pp. 1275 - 1300
Copyright
Copyright © Royal Aeronautical Society 2018 

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References

1.Zhu, B., Zhu, R. and Xiong, X. Fighter Plane Effectiveness Assessment, 1993, Aviation Industry Press, Beijing, China (in Chinese).Google Scholar
2.Ma, F., He, J., Ma, J. and Xia, S. Evaluation of urban green transportation planning based on central point triangle whiten weight function and entropy-AHP, Transportation Research Procedia, 2017, 25, pp 3638–3648.Google Scholar
3.Liu, S. and Li, H. A human factors integrated methods for weapon system effectiveness evaluation, Man-Machine-Environment System Engineering, 2016, Springer, Singapore, pp 469–479.Google Scholar
4.Dong, Y., Wang, L. and Zhang, H. Synthesized index model for fighter plane air combat effectiveness assessment, Acta Aeronautica Et Astronautica Sinica, 2006, 27, (6), pp 1084–1087, (in Chinese).Google Scholar
5.Wang, L., Zhang, H. and Xu, H. Multi-index synthesize evaluation model based on rough set theory for air combat efficiency, Acta Aeronautica Et Astronautica Sinica, 2008, 29, (4), pp 880–885, (in Chinese).Google Scholar
6.Forman, E.H. and Gass, S.I. The analytic hierarchy process: An exposition, Operations Research, 2001, 49, (4), pp 469–486.Google Scholar
7.Hwang, C.L. and Lin, M.J. Group Decision Making under Multiple Criteria: Methods and Applications, 1987, Springer-Verlag, Berlin, Germany.Google Scholar
8.Yang, G., Yang, J., Xu, D. and Khoveyni, M. A three-stage hybrid approach for weight assignment in MADM, Omega, 2016, 71, pp 93–105.Google Scholar
9.Deng, H., Yeh, C. and Willis Robert, J. Inter-company comparison using modified TOPSIS with objective weights, Computers & Operations Research, 2000, 27, (10), pp 963–973.Google Scholar
10.Valkenhoef, G.V. and Tervonen, T. Entropy-optimal weight constraint elicitation with additive multi-attribute utility models, Omega, 2016, 64, pp 1–12.Google Scholar
11.He, Y., Guo, H., Jin, M. and Ren, P. A linguistic entropy weight method and its application in linguistic multi-attribute group decision making, Nonlinear Dynamics, 2016, 84, (1), pp 399–404.Google Scholar
12.Jolliffe, I.T. Principle Component Analysis, 1986, Springer-Verlag, New York, US.Google Scholar
13.Wang, Y. and Luo, Y. Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making, Mathematical and Computer Modelling, 2010, 51, pp 1–12.Google Scholar
14.Diakoulaki, D., Mavrotas, G. and Papayannakis, L. Determining objective weights in multiple criteria problems: The critic method, Computers & Operations Research, 1995, 22, (7), pp 763–770.Google Scholar
15.Tahib, C.M.I.C., Yusoff, B., Abdullah, M.L. and Wahab, A. F. Conflicting bifuzzy multi-attribute group decision making model with application to flood control project, Group Decision & Negotiation, 2016, 25, (1), pp 157–180.Google Scholar
16.Liu, S., Chan, F.T.S. and Ran, W. Decision making for the selection of cloud vendor: An improved approach under group decision-making with integrated weights and objective/subjective attributes, Expert Systems with Applications, 2016, 55, pp 37–47.Google Scholar
17.Fu, C. and Yang, S. An attribute weight based feedback model for multiple attributive group decision analysis problems with group consensus requirements in evidential reasoning context, European J Operational Research, 2011, 212, (1), pp 179–189.Google Scholar
18.Dong, Y., Xiao, J., Zhang, H. and Wang, T. Managing consensus and weights in iterative multiple-attribute group decision making, Applied Soft Computing, 2016, 48, pp 80–90.Google Scholar
19.Chin, K. S., Fu, C. and Wang, Y. A method of determining attribute weights in evidential reasoning approach based on incompatibility among attributes, Computers & Industrial Engineering, 2015, 87, (C), pp 150–162.Google Scholar
20.Fu, C. and Xu, D. L. Determining attribute weights to improve solution reliability and its application to selecting leading industries, Annals of Operations Research, 2014, 245, (1-2), pp 401–426.Google Scholar
21.Sahoo, M., Sahoo, S., Dhar, A. and Pradhan, B. Effectiveness evaluation of objective and subjective weighting methods for aquifer vulnerability assessment in urban context, J Hydrology, 2016, 541, pp 1303–1315.Google Scholar
22.Ishibuchi, H. and Yamamoto, T. Rule weight specification in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems, 2005, 13, (4), pp 428–435.Google Scholar
23.Suo, M., Zhu, B., Zhou, D., An, R. and Li, S. Neighborhood grid clustering and its application in fault diagnosis of satellite power system, Proceedings of the Institution of Mech Engineers, Part G: J Aerospace Engineering, 2018, doi: 10.1177/0954410017751991.Google Scholar
24.Liang, J., Chin, K.S., Dang, C. and Yam, R.C.M. A new method for measuring uncertainty and fuzziness in rough set theory, Int J General Systems, 2002, 31, (4), pp 331–342.Google Scholar
25.Deng, J.L. Introduction to Grey system theory, Sci-Tech Information Services, 1989, pp 1–24.Google Scholar
26.Suo, M., An, R., Zhou, D. and Li, S. Grid-clustered rough set model for self-learning and fast reduction, Pattern Recognition Letters, 2018, 106, pp 61–68.Google Scholar
27.Hu, Q., Yu, D., Liu, J. and Wu, C. Neighborhood rough set based heterogeneous feature subset selection, Information Sciences, 2008, 178, (18), pp 3577–3594.Google Scholar
28.Yao, Y. A Partition model of granular computing, LNCS Transactions on Rough Sets, 2004, 1, pp 232–253.Google Scholar
29.Zhu, X.Z., Zhu, W. and Fan, X.N. Rough set methods in feature selection via submodular function, Soft Computing, 2016, pp 1–13.Google Scholar
30.Duda, R.O., Hart, P.E. and Stork, D.G. Pattern Classification (2nd Edition), Wiley-Interscience, 2000, pp 55–88.Google Scholar
31.Jiang, F. and Sui, Y. A novel approach for discretization of continuous attributes in rough set theory, Knowledge-Based Systems, 2015, 73, (1), pp 324–334.Google Scholar
32.Bongers, A. and Torres, J.L. Technological change in U.S. jet fighter aircraft, Research Policy, 2017, 43, (9), pp 1570–1581.Google Scholar
33.Wang, C., Shao, M., He, Q., Qian, Y. and Qi, Y. Feature subset selection based on fuzzy neighborhood rough sets, Knowledge-Based Systems, 2016, 111, pp 173–179.Google Scholar
34.Tan, P., Steinbach, M. and Kumar, V. Introduction to Data Mining, Posts & Telecom Press, 2011.Google Scholar
35.Kumar, S. and Byrne, W. Minimum Bayes-risk word alignments of bilingual texts, Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, 2002, pp 140-147.Google Scholar
36.Gonzlez-Rubio, J. and Casacuberta, F. Minimum Bayes risk subsequence combination for machine translation, Pattern Analysis & Applications, 2015, 18, (3), pp 523–533.Google Scholar
37.Suo, M., Zhu, B., Zhang, Y., An, R. and Li, S. Bayes risk based on Mahalanobis distance and Gaussian kernel for weight assignment in labeled multiple attribute decision making, Knowledge-Based Systems, 2018, 152, pp 26–39.Google Scholar