Basic Probability Assignment Generation Method and Application Based on Cloud Model

GUO Qiang; WEN Weilu; WANG Yani; QI Liangang; Kaliuzhny Mykola

doi:10.11999/JEIT211259

Volume 45 Issue 3

Mar. 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(3): 905-912

GUO Qiang, WEN Weilu, WANG Yani, QI Liangang, Kaliuzhny Mykola. Basic Probability Assignment Generation Method and Application Based on Cloud Model[J]. Journal of Electronics & Information Technology, 2023, 45(3): 905-912. doi: 10.11999/JEIT211259

Citation:

GUO Qiang, WEN Weilu, WANG Yani, QI Liangang, Kaliuzhny Mykola. Basic Probability Assignment Generation Method and Application Based on Cloud Model[J]. Journal of Electronics & Information Technology, 2023, 45(3): 905-912. doi: 10.11999/JEIT211259

Citation:

PDF( 4093 KB)

Basic Probability Assignment Generation Method and Application Based on Cloud Model

doi: 10.11999/JEIT211259

GUO Qiang^{1, 2},
WEN Weilu^{1, 2},
WANG Yani^{1, 2},
QI Liangang^{1, 3
,
,},
Kaliuzhny Mykola^{1, 4}

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin 150001, China
3.
Agile and Intelligent Computing Key Laboratory of Sichuan Province, Chengdu 610036, China
4.
Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine

Funds: The National Key R & D Plan (2018YFE0206500), The National Natural Science Foundation of China (62071140), The National Special for International Scientific and Technological Cooperation (2015DFR10220)

Received Date: 2021-11-12
Accepted Date: 2022-10-08
Rev Recd Date: 2022-10-07

Available Online: 2022-10-13

Publish Date: 2023-03-10

Abstract

Abstract

Basic Probability Assignment (BPA) has no fixed generative model in the application of evidence theory. To solve this problem, a BPA generation method based on cloud model is proposed. Firstly, based on the normal cloud model of the sample attributes, the BPA model function of the single subset proposition is constructed, and the model function of the composite subset is expressed as Gaussian function product fusion. Secondly, a method of dynamically measuring attribute weights based on test samples is proposed to take into account the reliability of information sources. Finally, the BPA is obtained by modifying the output result of the model function with attribute weights. The classification and recognition experiments of iris and other data sets show that this method has high recognition accuracy and is suitable for situations with fewer samples.
- Target recognition,
- Multi-source information fusion,
- Evidence theory,
- Basic Probability Assignment (BPA),
- Cloud model

FullText(HTML)

References(24)

References

[1]	DEMPSTER A P. Upper and lower probabilities induced by a multivalued mapping[J]. The Annals of Mathematical Statistics, 1967, 38(2): 325–339. doi: 10.1214/aoms/1177698950
[2]	MI Xiangjun, LÜ Tongxuan, TIAN Ye, et al. Multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target recognition system[J]. ISA Transactions, 2021, 112: 137–149. doi: 10.1016/j.isatra.2020.12.009
[3]	LIU Peide, ZHANG Xiaohong, and PEDRYCZ W. A consensus model for hesitant fuzzy linguistic group decision-making in the framework of Dempster–Shafer evidence theory[J]. Knowledge-Based Systems, 2021, 212: 106559. doi: 10.1016/j.knosys.2020.106559
[4]	尹东亮, 黄晓颖, 吴艳杰, 等. 基于云模型和改进D-S证据理论的目标识别决策方法[J]. 航空学报, 2021, 42(12): 324768. doi: 10.7527/S1000-6893.2020.24768 YIN Dongliang, HUANG Xiaoying, WU Yanjie, et al. Target recognition decision method based on cloud model and improved D-S evidence theory[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(12): 324768. doi: 10.7527/S1000-6893.2020.24768
[5]	王万请, 赵拥军, 黄洁, 等. 基于信息守恒的基本概率赋值概率转换方法[J]. 电子与信息学报, 2013, 35(2): 457–462. WANG Wanqing, ZHAO Yongjun, HUANG Jie, et al. A transformation of basic probability assignment to probability based on conservation of information[J]. Journal of Electronics & Information Technology, 2013, 35(2): 457–462 .
[6]	ZHANG Jingfei and DENG Yong. A method to determine basic probability assignment in the open world and its application in data fusion and classification[J]. Applied Intelligence, 2017, 46(4): 934–951. doi: 10.1007/s10489-016-0877-9
[7]	JING Ming and TANG Yongchuan. A new base basic probability assignment approach for conflict data fusion in the evidence theory[J]. Applied Intelligence, 2021, 51(2): 1056–1068. doi: 10.1007/s10489-020-01876-0
[8]	康兵义, 李娅, 邓勇, 等. 基于区间数的基本概率指派生成方法及应用[J]. 电子学报, 2012, 40(6): 1092–1096. doi: 10.3969/j.issn.0372-2112.2012.06.004 KANG Bingyi, LI Ya, DENG Yong, et al. Determination of basic probability assignment based on interval numbers and its application[J]. Acta Electronica Sinica, 2012, 40(6): 1092–1096. doi: 10.3969/j.issn.0372-2112.2012.06.004
[9]	WANG Shuning and TANG Yongchuan. An improved approach for generation of a basic probability assignment in the evidence theory based on Gaussian distribution[J]. Arabian Journal for Science and Engineering, 2022, 47(2): 1595–1607. doi: 10.1007/s13369-021-06011-w
[10]	肖建于, 童敏明, 朱昌杰, 等. 基于广义三角模糊数的基本概率赋值构造方法[J]. 仪器仪表学报, 2012, 33(2): 429–434. doi: 10.3969/j.issn.0254-3087.2012.02.027 XIAO Jianyu, TONG Minming, ZHU Changjie, et al. Basic probability assignment construction method based on generalized triangular fuzzy number[J]. Chinese Journal of Scientific Instrument, 2012, 33(2): 429–434. doi: 10.3969/j.issn.0254-3087.2012.02.027
[11]	崔家玮. 证据理论中基本概率指派生成与合成方法研究[D]. [硕士论文], 解放军信息工程大学, 2014: 15–24. CUI Jiawei. Research on basic probability assignment derivation and combination in evidence theory[D]. [Master dissertation], Information Engineering University, 2014: 15–24.
[12]	蒋雯, 陈运东, 汤潮, 等. 基于样本差异度的基本概率指派生成方法[J]. 控制与决策, 2015, 30(1): 71–75. doi: 10.13195/j.kzyjc.2013.1706 JIANG Wen, CHEN Yundong, TANG Chao, et al. Determination of basic probability assignment based on sample difference degree[J]. Control and Decision, 2015, 30(1): 71–75. doi: 10.13195/j.kzyjc.2013.1706
[13]	JIANG Wen, ZHUANG Miaoyan, XIE Chunhe, et al. Sensing attribute weights: A novel basic belief assignment method[J]. Sensors, 2017, 17(4): 721. doi: 10.3390/s17040721
[14]	SHAFER G A. A Mathematical Theory of Evidence[M]. Princeton: Princeton University Press, 1976.
[15]	FARAGHER R. Understanding the basis of the Kalman filter via a simple and intuitive derivation [Lecture Notes][J]. IEEE Signal Processing Magazine, 2012, 29(5): 128–132. doi: 10.1109/MSP.2012.2203621
[16]	刘树德. 概率论与数理统计[M]. 北京: 人民邮电出版社, 2019: 126–131. LIU Shude. Probability Theory and Mathematical Statistics[M]. Beijing: Post & Telecom Press, 2019: 126–131.
[17]	阎少宏, 王宏. 模糊数学基础及应用[M]. 北京: 化学工业出版社, 2018: 13–17. YAN Shaohong and WANG Hong. Basis and Application of Fuzzy Mathematics[M]. Beijing: Chemical Industry Press, 2018: 13–17.
[18]	李海林, 郭崇慧, 邱望仁. 正态云模型相似度计算方法[J]. 电子学报, 2011, 39(11): 2561–2567. LI Hailin, GUO Chonghui, and QIU Wangren. Similarity measurement between normal cloud models[J]. Acta Electronica Sinica, 2011, 39(11): 2561–2567.
[19]	GAO Shuguo, ZHAO Jun, LIU Yunpeng, et al. Research into power transformer health assessment technology based on uncertainty of information and deep architecture design[J]. Mathematical Problems in Engineering, 2021, 2021: 8831872. doi: 10.1155/2021/8831872
[20]	王栋. 基于数据融合的机载多传感器目标识别[D]. [硕士论文], 上海交通大学, 2010. WANG Dong. The airborne multi-sensor target recognition based on data fusion[D]. [Master dissertation], Shanghai Jiao Tong University, 2010.
[21]	FISHER R A. UCI machine learning repository: Iris[EB/OL]. https://archive-beta.ics.uci.edu/ml/datasets/iris, 1988.
[22]	FORINA M. UCI machine learning repository: Wine[EB/OL]. https://archive-beta.ics.uci.edu/ml/datasets/wine, 1991.
[23]	CHARYTANOWICZ M, NIEWCZAS J, KULCZYCKI P, et al. UCI machine learning repository: Seeds[EB/OL]. https://archive-beta.ics.uci.edu/ml/datasets/seeds, 2012.
[24]	赵卫东, 董亮. 机器学习[M]. 北京: 人民邮电出版社, 2018: 64–78. ZHAO Weidong and DONG Liang. Machine Learning[M]. Beijing: Post & Telecom Press, 2018: 64–78.