Target Priority Determination Methods by Interval-Valued Intuitionistic Fuzzy Sets with Unknown Attribute Weights

doi:10.1007/s12204-017-1880-y

Abstract

Abstract: The paper aims at the problem of multi-targets threat degree being hard to be evaluated accurately in complex air defense battlefield environments. Combined with multi-sensors information fusion and interval-valued intuitionistic fuzzy sets (IVIFS) theories, the target priority determination is studied. The score and accuracy functions of IVIFS are improved with thinking about the hesitating information in order to increase the rationality. Then, the influence factors of target priority and the nonlinear relationship between the influence factors and target priority are analyzed. Next, the algorithms for calculating the factor weights and sensor weights are given. Based on the theory of IVIFS and technique for order preference by similarity to an ideal solution (TOPSIS), two methods of target priority determination based on the IVIFS and TOPSIS are proposed. At last, an application example verifies the effectiveness and flexibility of the proposed algorithms.

Key words: target priority| interval-valued intuitionistic fuzzy set (IVIFS)| multi-sensors| information fusion| score and accuracy functions

摘要： The paper aims at the problem of multi-targets threat degree being hard to be evaluated accurately in complex air defense battlefield environments. Combined with multi-sensors information fusion and interval-valued intuitionistic fuzzy sets (IVIFS) theories, the target priority determination is studied. The score and accuracy functions of IVIFS are improved with thinking about the hesitating information in order to increase the rationality. Then, the influence factors of target priority and the nonlinear relationship between the influence factors and target priority are analyzed. Next, the algorithms for calculating the factor weights and sensor weights are given. Based on the theory of IVIFS and technique for order preference by similarity to an ideal solution (TOPSIS), two methods of target priority determination based on the IVIFS and TOPSIS are proposed. At last, an application example verifies the effectiveness and flexibility of the proposed algorithms.

关键词: target priority| interval-valued intuitionistic fuzzy set (IVIFS)| multi-sensors| information fusion| score and accuracy functions

CLC Number:

TP 391.7

XU Gongguo1* (徐公国), DUAN Xiusheng1 (段修生), LU Hao2 (吕豪). Target Priority Determination Methods by Interval-Valued Intuitionistic Fuzzy Sets with Unknown Attribute Weights[J]. Journal of shanghai Jiaotong University (Science), 2017, 22(5): 624-632.

References 23

[1]	SHEEBA K, IZHARUL H F. Prioritising municipalsolid waste management factors in India using fuzzyanalytic hierarchy process [J]. International Journal ofEnvironment & Waste Management, 2012, 10(4): 423-440.
[2]	NAYAGAMV L G,MURALIKRISHNAN S, SIVARAMANG. Multi-criteria decision-making method basedon interval-valued intuitionistic fuzzy sets [J]. ExpertSystems with Applications, 2011, 38(3): 1464-1467.
[3]	WANG W Z, LIU X W. The multi-attribute decisionmaking method based on interval-valued intuitionisticfuzzy Einstein hybrid weighted geometric operator[J]. Computers & Mathematics with Applications, 2013,66(10): 1845-1856.
[4]	GARIBALDI J M, OZEN T. Uncertain fuzzy reasoning:A case study in modelling expert decision making[J]. IEEE Transactions on Fuzzy Systems, 2007, 15(1):16-30.
[5]	TONG J, SHAN G L. Research on TOPSIS-basedvague sets used to solve targets priority levels [J]. FireControl & Command Control, 2012, 37(5): 140-143 (inChinese).
[6]	MENG G L, GONG G H. Algorithm of battlefieldsituation assessment based on Bayesian network andrough set theory [C]//Proceedings of the 7th InternationalConference on System Simulation and ScientificComputing. Chengdu, China: IEEE, 2008: 776-779.
[7]	YANG J, GAO W Y, LIU J. Threat assessmentmethod based on Bayesian network [J]. Journal of PLAUniversity of Science and Technology (Natural ScienceEdition), 2010, 11(1): 43-48 (in Chinese).
[8]	CHEN G, PARSA V. Loudness pattern-based speechquality evaluation using Bayesian modeling andMarkov chain Monte Carlo methods [J]. Journal of theAcoustical Society of America, 2007, 121(2): EL77-83.
[9]	AN X D, LIU G Q. Application of neural network inthe field of target threat evaluation [C]//InternationalJoint Conference on Neural Networks. Washington,USA: IEEE, 1999, 6: 4237-4240.
[10]	WANG G G, GUO L H, DUAN H, et al. The modeland algorithm for the target threat assessment basedon Elman-AdaBoost strong predictor [J]. Acta ElectronicaSinica, 2012, 40(5): 901-906 (in Chinese).
[11]	WANG F, PAN Q, GAO Q X, et al. A method oftarget priority determination based on neural network[J]. Electronics Optics & Control, 2003, 10(4): 38-41(in Chinese).
[12]	WANG B, WU J, XU D, et al. ITSM based targetsprioritizing for multi-asset attack [J]. Systems Engineeringand Electronics, 2010, 32(12): 2612-2617 (inChinese).
[13]	ATANASSOV K T. Intuitionistic fuzzy sets [J]. FuzzySets and Systems, 1986, 20(1): 87-96.
[14]	WANG Z J, LI K W, WANG W Z. An approach tomultiattribute decision making with interval-valued intuitionisticassessments and incomplete weights [J]. InformationSciences, 2009, 179(17): 3026-3040.
[15]	DONG G P, YOUNG C K, JIN H P, et al. Correlationcoefficient of interval-valued intuitionistic fuzzysets and its application to multiple attribute group decisionmaking problems [J]. Mathematical and ComputerModelling, 2009, 50(9/10): 1279-1293.
[16]	WANG W Z, LIU X W, QIN Y. Interval-valued intuitionisticfuzzy aggregation operators [J]. Journal ofSystems Engineering & Electronics, 2012, 04(4): 574-580.
[17]	YE J. Multicriteria fuzzy decision-making methodbased on a novel accuracy function under intervalvaluedintuitionistic fuzzy environment [J]. Expert Systemswith Applications, 2009, 36(3): 6899-6902.
[18]	IANCV I, COLHON M, DUPAC M. A fuzzy controllerwith various T-norms applied in robot navigation [J].WSEAS Transactions on Systems and Control, 2010,5(11): 882-891.
[19]	WEI G W, WANG J M, CHEN J. Potential optimalityand robust optimality in multiattribute decision analysiswith incomplete information: A comparative study[J]. Decision Support Systems, 2013, 55(3): 679-684.
[20]	PEI Z. Rational decision making models with incompleteweight information for production line assessment[J]. Information Sciences, 2013, 222(3): 696-716.
[21]	XU Z S. On similarity measures of interval-valued intuitionisticfuzzy sets and their application to patternrecognitions [J]. Journal of Southeast University (EnglishEdition), 2007, 23(1): 139-143.
[22]	LI D F. A ratio ranking method of triangular intuitionisticfuzzy numbers and its application to MADMproblems [J]. Computers and Mathematics with Applications,2010, 60(6): 1557-1570.
[23]	CHENG J H, TANG C H. An application of fuzzy Delphiand fuzzy AHP for multi-criteria evaluation on bicycleindustry supply chains [J]. WSEAS Transactionson Systems and Control, 2009, 4(1): 21-34.