IFCEM based recognition method for target with interval-overlapped hybrid attributes

doi:10.23919/JSEE.2022.000131

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (2): 408-421.doi: 10.23919/JSEE.2022.000131

• SYSTEMS ENGINEERING • Previous Articles

IFCEM based recognition method for target with interval-overlapped hybrid attributes

Xin GUAN¹(), Shuangming LI¹^,²^,*(), Guidong SUN³(), Haibin WANG¹()

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

Received:2021-12-17 Accepted:2022-07-12 Online:2023-04-18 Published:2023-04-18
Contact: Shuangming LI E-mail:gxtongwin@163.com;aminglishuang@126.com;sdwhsgd@163.com;hesonwhb@163.com
About author:
GUAN Xin was born in 1978. She received her B.E. degree in communication engineering from Liaoning University, Liaoning, China, in 1999. She received her M.E. and Ph.D. degrees in information and communication engineering from Naval Aeronautical Engineering Institute, Yantai, China, in 2002 and 2006, respectively. She is currently a professor in Naval Avation University. Her research interests are intelligent information processing and multi-source information fusion. E-mail: gxtongwin@163.com

LI Shuangming was born in 1986. He received his B.E. degree in automation and M.E. degree in control science and engineering from Naval Aeronautical Engineering Institute, Yantai, China, in 2009 and 2012, respectively. He is pursing his Ph.D. degree in information and communication engineering from Naval Avation University. His research interests include intelligent recognition and uncertain information processing. E-mail: aminglishuang@126.com

SUN Guidong was born in 1989. He received his B.E. and M.E. degrees from Naval Aeronautical Engineering Institute, Yantai, China, in 2012 and 2015, respectively, and Ph.D. degree from Naval Avation University, Yantai, China, in 2018. His research interests include complex electromagnetic environment, information fusion and intelligent decision making. E-mail: sdwhsgd@163.com

WANG Haibin was born in 1982. He received his B.E. degree in electronic information engineering from Harbin Engineering University, Harbin, China, in 2005 and M.E. degree in electronic science and technology from National University of Defense Technology, Changsha, China, in 2007, respectively. He is pursing his Ph.D. degree in information and communication engineering from Naval Avation University. His research interest is uncertain information processing. E-mail: hesonwhb@163.com
First author contact:
Co-first author
Supported by:
This work was supported by the Youth Foundation of the 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)

Abstract

Abstract:

When the attributes of unknown targets are not just numerical attributes, but hybrid attributes containing linguistic attributes, the existing recognition methods are not effective. In addition, it is more difficult to identify the unknown targets densely distributed in the feature space, especially when there is interval overlap between attribute measurements of different target classes. To address these problems, a novel method based on intuitionistic fuzzy comprehensive evaluation model (IFCEM) is proposed. For numerical attributes, targets in the database are divided into individual classes and overlapping classes, and for linguistic attributes, continuous interval-valued linguistic term set (CIVLTS) is used to describe target characteristic. A cloud model-based method and an area-based method are proposed to obtain intuitionistic fuzzy decision information of query target on numerical attributes and linguistic attributes respectively. An improved inverse weighted kernel fuzzy c-means (IWK-FCM) algorithm is proposed for solution of attribute weight vector. The possibility matrix is applied to determine the identity and category of query target. Finally, a case study composed of parameter sensitivity analysis, recognition accuracy analysis. and comparison with other methods, is taken to verify the superiority of the proposed method.

Key words: intuitionistic fuzzy comprehensive evaluation model (IFCEM), interval overlapping, cloud model, area-based method, inverse weighted kernel fuzzy c-means (IWK-FCM)

Xin GUAN, Shuangming LI, Guidong SUN, Haibin WANG. IFCEM based recognition method for target with interval-overlapped hybrid attributes[J]. Journal of Systems Engineering and Electronics, 2023, 34(2): 408-421.

Figures/Tables 17

Fig 1

Fig 2

Fig 3

Fig 4

Fig 5

Table 1

Fig 6

Fig 7

Fig 8

Table 2

Fig 9

Table 3

Table 4

Fig 10

Fig 11

Table 5

Table 6

References 32

1	WOJTOWICZ Z, DEDEO S. From probability to consilience: how explanatory values implement Bayesian reasoning. Trends in Cognitive Sciences, 2020, 24(12): 981−993.
2	KINNEAR B, HAGEDORN P A, KELLEHER M, et al. Integrating Bayesian reasoning into medical education using smartphone apps. Diagnosis, 2019, 6(2): 85−89.
3	GUO J C, LIU X, CUI S L. Target recognition algorithm for compound scanning imaging based on fuzzy theory. Journal of Physics: Conference Series, 2020, 1656: 012035.
4	MEHTA P, DE A, SHASHIKIRAN D, et al. Radar target classification using improved Dempster-Shafer theory. The Journal of Engineering, 2019, 2019 (21): 7872−7875.
5	ZHANG H B, WANG X, WU X T, et al. Airborne multi-sensor target recognition method based on weighted fuzzy reasoning network and improved DS evidence theory. Journal of Physics: Conference Series, 2020, 1550: 032112.
6	WANG H, GUO L L, LIN Y. Recognition method of software defined radio signal based on evidence theory and interval grey relation. Proc. of the IEEE International Conference on Software Quality, Reliability and Security, 2017: 233−237.
7	WAN S P. Method of interval deviation degree for uncertain multi-sensor target recognition. Control and Decision, 2009, 24(9): 1306−1309.
8	GUAN X, SUN G D, GUO Q, et al. Radar emitter parameter recognition based on interval number and evidence theory. Systems Engineering and Electronics, 2014, 36 (7): 1269−1274. (in Chinese)
9	LIU H J, LIU Z, JIANG W L, et al Approach based on combination of vector neural networks for emitter identification. IET Signal Process, 2010, 4 (2): 137- 148. doi: 10.1049/iet-spr.2008.0247
10	GUO Q, HE Y. DSm evidence modeling and radar emitter fusion recognition method based on cloud model. Journal of Electronics & Information Technology, 2015, 37 (8): 1779 − 1785. (in Chinese)
11	LIU H J, LIU Z, JIANG W L, et al. A method for emitter recognition based on cloud model. Journal of Electronics & Information Technology, 2009, 31(9): 2079 − 2083. (in Chinese)
12	ATANASSOV K Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 1986, 20 (1): 87- 96. doi: 10.1016/S0165-0114(86)80034-3
13	XU Z S. Intuitionistic fuzzy aggregation operators. IEEE Trans. on Fuzzy Systems, 2007, 15(6): 1179−1187.
14	WEI A P, LI D F, LIN P P, et al. An information-based score function of interval-valued intuitionistic fuzzy sets and its application in multiattribute decision making. Soft Computing, 2021, 25: 1913−1923.
15	DONG Y K, ZHANG J T, LI Z, et al. Combination of evidential sensor reports with distance function and belief entropy in fault diagnosis. International Journal of Computer, Communications & Control, 2019, 14(3): 293−307.
16	XIAO F Y. A distance measure for intuitionistic fuzzy sets and its application to pattern classification problems. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2020, 51(6): 3980−3992.
17	MAHANTA J, PANDA S. A novel distance measure for intuitionistic fuzzy sets with diverse applications. International Journal of intelligent Systems. 2021, 36: 615−627.
18	MUKESHIMANA M C, ZHAO Z Y, AHMAD M, et al. Analysis on barriers to biogas dissemination in Rwanda: AHP approach. Renewable Energy, 2021, 163(1): 1127−1137.
19	SUN Y, YAO P Y, WAN L J, et al. Multiple attribute decision making method based on weights aggregation and relative dominance relation. Control and Decision, 2017, 32 (2): 317−322. (in Chinese)
20	YU L, LAI K K. A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support. Decision Support Systems, 2011, 51(5): 307−315.
21	CHEN P Y. Effects of the entropy weight on TOPSIS. Expert Systems with Applications, 2021, 168(4): 114186.
22	MENG F Y, TANG J, PEDRYCZ W. Dual hesitant fuzzy decision making in optimization models. Computers & Industrial Engineering, 2021, 154(4): 107103.
23	YANG Z, WANG Y F, YANG K. The stochastic decision making framework for long-term multi-objective energy-water supply-ecology operation in parallel reservoirs system under uncertainties. Expert Systems with Applications, 2022, 187(1): 115907.
24	YU S M, WANG J, WANG J Q, et al. A multi-criteria decision-making model for hotel selection with linguistic distribution assessments. Applied Soft Computing, 2018, 67: 741−755.
25	LIAO H C, WU X L, LIANG X D, et al. A continuous interval-valued linguistic ORESTE method for multi-criteria group decision making. Knowledge-Based Systems, 2018, 153(8): 65−77.
26	LEI Y J, ZHAO J, LI Y L, et al. Intuitionistic fuzzy set theory and its application. Beijing: Science Press, 2014. (in Chinese)
27	WANG G Y, XU C L, LI D Y. Generic normal cloud model. Information Sciences, 2014, 280(10): 1−15
28	VAPNIK V. The nature of statistical learning theory. Berlin: Springer Verlag, 1995.
29	LI T H, ZHANG L Y, LU W, et al. Interval kernel fuzzy C-Means clustering of incomplete data. Neurocomputing, 2017, 237(5): 316−331.
30	LIU A O, ZHAO D N, LI T J. A data classification method based on particle swarm optimisation and kernel function extreme learning machine. Enterprise Information Systems, 2021. DOI:10.1080/17517575.2021.1913764.
31	LI S M, GUAN X, ZHAO J, et al. A methodology for target recognition with parameters of interval cross type. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1307−1316. (in Chinese)
32	FEI S W Fault Diagnosis of bearing based on wavelet packet transform-phase space reconstruction-singular value decomposition and SVM classifier. Arabian Journal for Science & Engineering, 2017, 42 (5): 1967- 1975.

Radar class	F₁ /MHz	F₂ /μs	F₃ /μs	F_L
R₁	[4 940,5 160]	[3 680,3 750]	[0.6,1.2]	s₋₂
R₂	[5 000,5 220]	[3 630,3 700]	[0.2,0.5]	s₋₁
R₃	[5 100,5 420]	[3 580,3 650]	[0.4,0.7]	s₀
R₄	[5 400,5 520]	[3 730,3 800]	[0.6,0.9]	s₁
R₅	[5 480,5 620]	[3 490,3 600]	[1.0,1.4]	s₂

Object	F₁	F₂	F₃	F_L
Object 1	4 955	3 736	0.60	25% higher than s₋₂, 10% lower than s₋₁
Object 2	5 090	3 635	0.45	15% higher than s₋₁, 18% lower than s₀
Object 3	5 408	3 599	0.65	30% higher than s₀, 5% lower than s₁
Object 4	5 481	3 765	0.77	5% higher than s₁, 20% lower than s₂
Object 5	5 554	3 599	1.04	25% higher than s₁, 10% lower than s₂

Parameter	Proposed cloud model	Cloud model in [11]			Cloud model in [10]
Parameter	Proposed cloud model	Group 1	Group 2	Group 3	Group 1	Group 2	Group 3
Correct recognition ratio	97.75	93.60	89.35	87.95	90.60	92.40	84.15

Method	Correct ratio
Proposed weight method	97.75
Weight method in [11]	6.05
Weight method in [31]	83.55
Mean weight	83.1

Method	Correct ratio
Method	Training data	Testing data
Proposed method	98	97.75
TOPSIS-IFS-CM	77.5	77.80
DS-PCRS-CM	75	76.40
SVM	94.3	94.05
PSO-KELM	100	92.40

IFCEM based recognition method for target with interval-overlapped hybrid attributes

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 17

References 32

Related Articles 3

Recommended Articles

Metrics

Comments

Measurement error	Proposed method			TOPSIS-IFS-CM			DS-PCRS-CM			PSO-KELM			SVM
Measurement error	Case 1	Case 2	Case 3	Case 1	Case 2	Case 3	Case 1	Case 2	Case 3	Case 1	Case 2	Case 3	Case 1	Case 2	Case 3
2	83.80	54.80	92.00	59.30	47.00	71.35	61.30	53.00	64.55	47.65	58.45	92.4	26.5	37.1	93.90
5	82.95	49.30	90.60	49.95	37.05	70.90	54.30	48.30	62.05	29.5	44.5	92.9	20.1	24.7	93.9
8	81.55	45.30	90.20	43.75	32.35	68.60	53.50	49.10	62.25	23.4	36.65	92.65	19.9	24.15	93.95
12	81.30	41.70	90.05	40.25	28.70	67.95	53.00	52.00	59.90	22.35	29.30	92.45	20.15	22.35	93.85
15	79.10	41.20	89.50	36.25	26.75	67.50	53.80	50.75	58.50	20.55	27.85	92.2	19.85	21.4	93.9
20	78.40	39.20	89.10	35.95	25.50	64.65	52.30	51.60	55.25	20.30	26.95	92.45	20.65	21.3	93.9

[1]	Xiaolong XU, Qitong ZHANG, Yiqi MOU, Xinyuan LU. Server load prediction algorithm based on CM-MC for cloud systems [J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 1069-1078.
[2]	Qiang Guo, Pulong Nan, and Jian Wan. Signal classification method based on data mining for multi-mode radar [J]. Journal of Systems Engineering and Electronics, 2016, 27(5): 1010-1017.
[3]	Mingwei Li, Haigui Kang, Pengfei Zhou, and Weichiang Hong. Hybrid optimization algorithm based on chaos, cloud and particle swarm optimization algorithm [J]. Journal of Systems Engineering and Electronics, 2013, 24(2): 324-334.