An interpretation of intuitionistic fuzzy sets in terms of evidence theory: Decision making aspect

Dymova, Ludmila; Sevastjanov, Pavel

doi:10.1016/j.knosys.2010.04.014

Cited by 123 publications

(74 citation statements)

References 64 publications

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“…Dymova and Sevastjanov (2010) in the form of group psychological counseling on 15 students were two months of counseling, results showed that the control group and the experimental group before treatment, the difference was not significant [14]. Therefore, this study suggests that group counseling can effectively improve coping skills and improve the decision-making skills of the career.…”

Section: Online Education and Decision-makingmentioning

confidence: 76%

An Improved Evaluation of College Students' Decision-making Behavior Based on Online Survey: Improving Management Performance through Network

Dai¹

2016

IJSH

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Section: Online Education and Decision-makingmentioning

confidence: 76%

An Improved Evaluation of College Students' Decision-making Behavior Based on Online Survey: Improving Management Performance through Network

Dai¹

2016

IJSH

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“…DS theory can handle imprecise and imperfect information with its unique probability quantifications and valid uncertainty measurement. Thus, compared with probability theory, DS theory has broader application areas, like expert systems [26,27], decision-making [28,29], fault diagnosis [30,31], target tracking [32], image restorations [33], etc.…”

Section: Review Of Ds Theorymentioning

confidence: 99%

Conflicting Information Fusion Based on an Improved DS Combination Method

Chen

Jiang

et al. 2017

Symmetry

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An effective and reliable fusion method for conflicting information is proposed in this paper. Compared with a single-sensor system, a multi-sensor fusion system can comprehensively combine the redundancy and complementarity of multi-sensor information to obtain better system performance. Hence, the multi-sensor fusion system has become one of the research hotspots. However, due to lack knowledge about the measurement environment and limited sensor accuracy, the multi-sensor system inevitably appears to have imperfect, uncertain and inconsistent information. To solve the problem, we introduce one powerful uncertainty reasoning method: Dempster-Shafer theory (DS theory). With convincing measurement and a forceful combination of uncertain information, DS theory is widely applied in various fields, like decision-making, expert systems, target tracking, monitoring systems, etc. Nevertheless, DS theory will produce counter-intuitive fusion results when the pieces of evidence are highly conflicting. To address this issue, we raise an improved DS combination method for conflicting information fusion in this paper. First of all, the modified Minkowski distance function and the betting-commitment distance function are separately employed to revise potentially conflicting pieces of evidence. The procedure availably solves the conflicting situations caused by unreliable and imprecise evidence sources, which enhances the consistency among pieces of evidence. Then, based on two revised pieces of evidence, a conflicting redistribution strategy based on locally conflicting analyses is put forward. The approach dexterously combines two revised pieces of evidence to avoid conflicting situations caused by compulsive normalization, which further improves the accuracy and convergence speed of the multi-sensor fusion system. Finally, two experimental analyses with consistent information and conflicting information reveal the remarkable effectiveness and priority of the proposed algorithm for the multi-sensor fusion system. Consequently, this paper has certain value for the multi-sensor fusion system.

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“…The strong connection between the intuitionistic fuzzy sets and DST was presented in [31]. This connection allows one to directly apply Dempster's rule of combination in MCDM issues in order to aggregate local criteria with intuitionistic fuzzy values.…”

Section: Basic Definitionsmentioning

confidence: 99%

An approach to exponentiation with interval-valued power

Tikhonenko-Kędziak¹,

Kurkowski²

2016

J. Appl. Math. Comput. Mech.

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Abstract. The main aim of the work is introducing an operation of raising intuitionistic fuzzy values to intuitionistic fuzzy power, which not requiring to conversion of intuitionistic fuzzy values. Introducing an operation of raising intuitionistic fuzzy values to intuitionistic fuzzy power, which does not require conversion of intuitionistic fuzzy values is the main aim of the work. It is known that, in the classical intuitionistic fuzzy sets theory, the use of all aggregation modes is not always possible because of the lack of definition of raising intuitionistic fuzzy values to intuitionistic fuzzy power. Therefore, the specific aim of the work is to present the heuristic method of raising intuitionistic fuzzy values to intuitionistic fuzzy power, and the consideration of its properties.

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An interpretation of intuitionistic fuzzy sets in terms of evidence theory: Decision making aspect

Cited by 123 publications

References 64 publications

An Improved Evaluation of College Students' Decision-making Behavior Based on Online Survey: Improving Management Performance through Network

An Improved Evaluation of College Students' Decision-making Behavior Based on Online Survey: Improving Management Performance through Network

Conflicting Information Fusion Based on an Improved DS Combination Method

An approach to exponentiation with interval-valued power

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