Ranking Z-numbers with an improved ranking method for generalized fuzzy numbers

Jiang, Wen; Xie, Chunhe; Luo, Yu; Tang, Yongchuan

doi:10.3233/jifs-16139

Cited by 89 publications

(44 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…From Figure 3, we can see that the reliability is proportional to the evaluation of the expert using constraint of considering an optimistic attitude (orness measure = 0.5). .0352] by the maximum entropy method using Equation (5), which can be regarded as the probability of the random…”

Section: Numerical Examplesmentioning

confidence: 99%

“…Z‐number combined with constraint and reliability is proposed by Zadeh, which is an increasingly hot topic in the research field of fuzzy set . Z‐number is a good framework to express the thinking and knowledge of human flexibly . For a fuzzy user, the membership function of the constraint is relatively easy to obtain, whereas the possibility distribution of the reliability on the assigned membership function of the constraint is not easy to achieve.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4] Z-number is a good framework to express the thinking and knowledge of human flexibly. [5][6][7] For a fuzzy user, the membership function of the constraint is relatively easy to obtain, whereas the possibility distribution of the reliability on the assigned membership function of the constraint is not easy to achieve. From the literature review, limited work has been done to determine Z-number by a more reasonable way, especially for the reliability, but given by the decision maker directly.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Generating Z-number based on OWA weights using maximum entropy

Kang

Deng

Hewage

et al. 2018

Int. J. Intell. Syst.

View full text Add to dashboard Cite

In the application of Z‐number, how to generate Z‐number is a significant and open issue. In this paper, we proposed a method of generating Z‐number based on the OWA weights using maximum entropy considering the attitude (preference) of the decision maker. Some numerical examples are used to illustrate the effectiveness of the proposed method. Results show that the attitude (preference) of the decision maker can give an optimal possibility distribution of the reliability for Z‐number using maximum entropy.

show abstract

Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Generating Z-number based on OWA weights using maximum entropy

Kang

Deng

Hewage

et al. 2018

Int. J. Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…A few ranking of Z‐number techniques are introduced in Refs. . In the previous work, we used a convex combination of fuzzy numbers and method for ranking of Z‐numbers was suggested.…”

Section: Introductionmentioning

confidence: 99%

Two new methods for ranking of Z-numbers based on sigmoid function and sign method

Ezadi

Allahviranloo

Mohammadi

2018

Int. J. Intell. Syst.

View full text Add to dashboard Cite

This paper introduces two processes of ranking methods on Z‐numbers that are effectively able to deal with uncertain decision‐making data. Decision making is based on recommended Z‐ numbers. For this purpose first, the Z‐number is transformed to a fuzzy number and then the ranking method by using the sigmoid function and the sign method is used to mention fuzzy numbers. For the next step, the method is extended to related Z‐numbers. Finally, we use it to prioritize the items and solve some examples.

show abstract

“…Jiang et al [38] presented a method for ranking generalized fuzzy numbers. Here, the weight of centroid points, the spreads of fuzzy numbers, and degrees of fuzziness are taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Food Security Risk Level UsingZ-Number-Based Fuzzy System

Abiyev

Uyar

İlhan

et al. 2018

Journal of Food Quality

View full text Add to dashboard Cite

Fuzzy logic systems based on If-en rules are widely used for modelling of the systems characterizing imprecise and uncertain information. ese systems are basically based on type-1 fuzzy sets and allow handling the uncertain and imprecise information to some degree in the developed models. Zadeh extended the concept of fuzzy sets and proposed Z-number characterized by two components, constraint and reliability parameters, which are an ordered pair of fuzzy numbers. Here, the rst component is used to represent uncertain information, and the second component is used to evaluate the reliability or the con dence in truth. Znumber is an e ective approach to solving uncertain problems. In this paper, Z-number-based fuzzy system is proposed for estimation of food security risk level. To construct fuzzy If-en rules, the basic parameters cereal yield, cereal production, and economic growth a ecting food security are selected, and the relationship between these input parameters and risk level are determined through If-en fuzzy rules. e fuzzy interpolative reasoning is proposed for construction of inference mechanism of a Z-number-based fuzzy system. e designed system is tested using Turkey cereal data for assessing food security risk level and prediction periods of the food supply.

show abstract

Ranking Z-numbers with an improved ranking method for generalized fuzzy numbers

Cited by 89 publications

References 30 publications

Generating Z-number based on OWA weights using maximum entropy

Generating Z-number based on OWA weights using maximum entropy

Two new methods for ranking of Z-numbers based on sigmoid function and sign method

Estimation of Food Security Risk Level UsingZ-Number-Based Fuzzy System

Contact Info

Product

Resources

About