Combination for Conflicting Interval-Valued Belief Structures with CSUI-DST Method

LI Shuangming; GUAN Xin; YI Xiao; SUN Guidong

doi:10.1049/cje.2021.00.214

Volume 31 Issue 5

Sep. 2022

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2022 > 31(5): 980-990

LI Shuangming, GUAN Xin, YI Xiao, et al., “Combination for Conflicting Interval-Valued Belief Structures with CSUI-DST Method,” Chinese Journal of Electronics, vol. 31, no. 5, pp. 980-990, 2022, doi: 10.1049/cje.2021.00.214

Citation:

LI Shuangming, GUAN Xin, YI Xiao, et al., “Combination for Conflicting Interval-Valued Belief Structures with CSUI-DST Method,” Chinese Journal of Electronics, vol. 31, no. 5, pp. 980-990, 2022, doi: 10.1049/cje.2021.00.214

Citation:

PDF( 3249 KB)

Combination for Conflicting Interval-Valued Belief Structures with CSUI-DST Method

doi: 10.1049/cje.2021.00.214

LI Shuangming^{1, 2
,},
GUAN Xin^1
,,
YI Xiao¹,
SUN Guidong^3
,

1.
Naval Aviation University, Yantai 264001, China
2.
Unit 92941 of PLA, Huludao 125001, China
3.
Unit 32801 of PLA, Beijing 100082, China

Funds: This work was supported by the Youth Foundation of National Science Foundation of China (62001503), the Excellent Youth Scholar of the National Defense Science and Technology Foundation of China (2017-JCJQ-ZQ-003), and the Special Fund for Taishan Scholar Project (ts201712072)

More Information

Author Bio:
was born in 1986. He is pursing the Ph.D. degree in information and communication engineering from Naval Aviation University. His research interests include intelligent recognition and uncertain information processing. (Email: aminglishuang@126.com)

(corresponding author) was born in 1978. She is a Ph.D. and Professor in the Naval Aviation University. Her research interests are intelligent information processing and multi-source information fusion. (Email: gxtongwin@163.com)

(co-corresponding author) was born in 1989. He received the Ph.D. degree from Naval Aviation University. His research interests include complex electromagnetic environment, information fusion, and intelligent decision making. (Email: sdwhsgd@163.com)
Received Date: 2021-06-19
Accepted Date: 2021-11-25

Available Online: 2021-12-10

Publish Date: 2022-09-05

Abstract

Abstract

Since the basic probability of an interval-valued belief structure (IBS) is assigned as interval number, its combination becomes difficult. Especially, when dealing with highly conflicting IBSs, most of the existing combination methods may cause counter-intuitive results, which can bring extra heavy computational burden due to nonlinear optimization model, and lose the good property of associativity and commutativity in Dempster-Shafer theory (DST). To address these problems, a novel conflicting IBSs combination method named CSUI (conflict, similarity, uncertainty, intuitionistic fuzzy sets)-DST method is proposed by introducing a similarity measurement to measure the degree of conflict among IBSs, and an uncertainty measurement to measure the degree of discord, non-specificity and fuzziness of IBSs. Considering these two measures at the same time, the weight of each IBS is determined according to the modified reliability degree. From the perspective of intuitionistic fuzzy sets, we propose the weighted average IBSs combination rule by the addition and number multiplication operators. The effectiveness and rationality of this combination method are validated with two numerical examples and its application in target recognition.
- Conflicting IBSs,
- Similarity and uncertainty measure,
- Addition and number multiplication operators,
- Weight

FullText(HTML)

References(29)

References

[1]	J. Wang and Q. Yu, “A dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator,” Applied Intelligence, vol.50, no.11, pp.3837–3851, 2020. doi: 10.1007/s10489-020-01739-8
[2]	S. Hao, Y. Chen, B. Hu, et al., “A classifier-combined method based on D-S evidence theory for the land cover classification of the Tibetan Plateau,” Environmental Science and Pollution Research, vol.28, no.6, pp.1–13, 2021.
[3]	L. S. Fernández-Mellado and M. Vasile, “On the use of machine learning and evidence theory to improve collision risk management,” Acta Astronautica, vol.181, pp.694–706, 2021. doi: 10.1016/j.actaastro.2020.08.004
[4]	D. Liu, “Prediction of network security based on DS evidence theory,” ETRI Journal, vol.42, no.5, pp.802–807, 2020.
[5]	E. Koksalmis and O. Kabak, “Sensor fusion based on Dempster-Shafer theory of evidence using a large scale group decision making approach,” International Journal of Intelligent Systems, vol.35, no.7, pp.1126–1162, 2020. doi: 10.1002/int.22237
[6]	J. Meng, D. Fu, and Y. Tang, “Belief-peaks clustering based on fuzzy label propagation,” Applied Intelligence, vol.50, no.4, pp.1259–1271, 2020. doi: 10.1007/s10489-019-01576-4
[7]	D. Zhang, Z. Peng, G. Ning, et al., “Positioning accuracy reliability of industrial robots through probability and evidence theories,” Journal of Mechanical Design, vol.143, no.1, article no.011704, 2021. doi: 10.1115/1.4047436
[8]	Y. Lin, Y. Li, X. Yin, et al., “Multisensor fault diagnosis modeling based on the evidence theory,” IEEE Transactions on Reliability, vol.67, no.2, pp.513–521, 2018. doi: 10.1109/TR.2018.2800014
[9]	Y. Tang, D. Wu, and Z. Liu, “A new approach for generation of generalized basic probability assignment in the evidence theory,” Pattern Analysis and Applications, vol.24, pp.1007–1023, 2021. doi: 10.1007/s10044-021-00966-0
[10]	G. Sun, X. Guan, X. Yi, et al., “Conflict evidence measurement based on the weighted separate union kernel correlation coefficient,” IEEE Access, vol.6, pp.30458–30472, 2018. doi: 10.1109/ACCESS.2018.2844201
[11]	J. Abellán, S. Moral-García, and M. D. Benítez, “Combination in the theory of evidence via a new measurement of the conflict between evidences,” Expert Systems with Applications, vol.178, article no.114978, 2021.
[12]	Z. Ren and H. Liao, “Combining conflicting evidence by constructing evidence’s angle-distance ordered weighted averaging pairs,” International Journal of Fuzy Systems, vol.23, no.2, pp.494–505, 2021. doi: 10.1007/s40815-020-00964-0
[13]	J. An, M. Hu, L. Fu, et al., “A novel fuzzy approach for combining uncertain conflict evidences in the Dempster-Shafer theory,” IEEE Access, vol.7, pp.7481–7501, 2019. doi: 10.1109/ACCESS.2018.2890419
[14]	Y. Wang, J. Yang, and D. Xu, “On the combination and normalization of interval-valued belief structures,” Information Sciences, vol.177, pp.1230–1247, 2007. doi: 10.1016/j.ins.2006.07.025
[15]	P. Sevastianov, L. Dymova, and P. Bartosiewicz, “A framework for rule-base evidential reasoning in the interval setting applied to diagnosing type 2 diabetes,” Expert Systems with Applications, vol.39, pp.4190–4200, 2012. doi: 10.1016/j.eswa.2011.09.115
[16]	H. Seiti, A. Hafezalkotob, S. E. Najafi, et al., “A risk-based fuzzy evidential framework for FMEA analysis under uncertainty: An interval-valued DS approach,” Journal of Intelligent & Fuzzy Systems, vol.35, pp.1419–1430, 2018.
[17]	Y. Song, X. Wang, L. Lei, et al., “Combination of interval-valued belief structures based on intuitionistic fuzzy set,” Knowledge-Based Systems, vol.67, pp.61–70, 2014. doi: 10.1016/j.knosys.2014.06.008
[18]	X. Xu, D. Li, and Z. Liu, “Weighted interval-valued belief structures on atanassov’s intuitionistic fuzzy sets,” Quantitative Logic and Soft Computing, vol.510, pp.539–551, 2017.
[19]	X. Zhang and Y. Wang, “A hybrid multi-attribute decision-making method based on interval belief structure,” Control and Decision, vol.34, no.1, pp.180–188, 2019. (in Chinese)
[20]	X. Zhang, Y. Wang, and S. Chen, “Group decision making method based on evidential reasoning rule under conflicting interval belief structures,” Journal of South China University of Technology (Natural Science Edition), vol.48, no.6, pp.134–142, 2020. (in Chinese)
[21]	X. Zhang, Y. Wang, S. Chen, et al., “On the combination and normalization of conflicting interval-valued belief structures,” Computers & Industrial Engineering, vol.137, article no.106020, 2019.
[22]	W. Sun, A. Xu, and W. Li, “Research on combination of interval-valued belief structures based on features of evidence,” Systems Engineering and Electronics, vol.38, no.12, pp.2790–2798, 2016. (in Chinese)
[23]	J. Wang and L. Yu, “An extended evidential reasoning approach with confidence interval belief structure,” Journal of Intelligent & Fuzzy System, vol.42, no.3, pp.2939–2956, 2022. doi: 10.3233/JIFS-210286
[24]	S. Chen and Y. Wang, “Conflicting evidence combination of interval-valued belief structures,” Systems Engineering - Theory & Practice, vol.34, no.1, pp.256–261, 2014. (in Chinese)
[25]	H. Feng, X. Xu, and C. Wen, “A new fusion method of conflicting interval evidence based on the similarity measure of evidence,” Journal of Electronics & Information Technology, vol.34, no.4, pp.851–857, 2012.
[26]	S. Chen, Y. Wang, H. Shi, et al., “Alliance-based evidential reasoning approach with unknown evidence weights,” Expert Systems with Applications, vol.78, pp.193–207, 2017. doi: 10.1016/j.eswa.2017.01.043
[27]	T. Denoeux, “Reasoning with imprecise belief structures,” International Journal of Approximate Reasoning, vol.20, no.1, pp.79–111, 1999. doi: 10.1016/S0888-613X(00)88944-6
[28]	Y. Song, X. Wang, L. Lei, et al., “Uncertainty measure for interval-valued belief structures,” Measurement, vol.80, pp.241–250, 2016. doi: 10.1016/j.measurement.2015.11.032
[29]	F. Xiao and B. Qin, “A weighted combination method for conflicting evidence in multi-sensor data fusion,” Sensors, vol.18, article no.1487, 2018. doi: 10.3390/s18051487