An improved method to rank generalized fuzzy numbers with different left heights and right heights

Jiang, Wen; Luo, Yu; Qin, Xin; Zhan, Jun

doi:10.3233/ifs-151639

Cited by 78 publications

(52 citation statements)

References 20 publications

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“…A generalized fuzzy number A = (a, b, c, d; w) is described as any fuzzy subset of the real line R with membership function µ A which has the following properties [14]:…”

Section: Generalized Fuzzy Numbersmentioning

confidence: 99%

Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers

Jiang¹,

Yan

Luo

et al. 2015

INT J COMPUT COMMUN

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Dempster-Shafer theory of evidence has been widely used in many data fusion application systems. However, how to determine basic probability assignment, which is the main and the first step in evidence theory, is still an open issue. In this paper, an improved method to determine the similarity measure between generalized fuzzy numbers is presented. The proposed method can overcome the drawbacks of the existing similarity measures. Then, we propose a new method for obtaining basic probability assignment (BPA) based on the proposed similarity measure method between generalized fuzzy numbers. Finally, the efficiency of the proposed method is illustrated by the classification of Iris data.

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“…A generalized fuzzy number A = (a, b, c, d; w) is described as any fuzzy subset of the real line R with membership function µ A which has the following properties [14]:…”

Section: Generalized Fuzzy Numbersmentioning

confidence: 99%

Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers

Jiang¹,

Yan

Luo

et al. 2015

INT J COMPUT COMMUN

Self Cite

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“…It provided a unified framework for decision making under uncertainly environment where different decision criteria such as optimistic, pessimistic and equally likely were characterized by different OWA operator weights (Ma, Xiong, & Luo, 2013;Xia & Xu, 2011;Xia, Xu, & Chen, 2013;Xu & Yager, 2010;Yager & Alajlan, 2013). To address decision making issues under uncertain environment, fuzzy sets theory (Jiang, Luo, Qin & Zhan, 2015a;Jiang, Yang, Luo & Qin, 2015b;Kahraman, Onar & Oztaysi, 2015;Liu, Liu & Lin, 2013a;Liu, 2014;Rikhtegar et al, 2014;Zadeh, 1965) and evidence theory (Bandyopadhyay & Bhattacharya, 2015;Dempster, 1967;Deng, Mahadevan & Zhou, 2015;Deng, 2015;Fu & Yang, 2012;Kabir, Tesfamariam, Francisque & Sadiq, 2015;Liang, Pedrycz, Liu & Hu, 2015;Liu, Pan & Dezert, 2013b;Liu, Pan, Dezert & Mercier, 2014;Shafer, 1976;Su, Mahadevan, Xu & Deng, 2015;Wang, Dai, Chen & Meng, 2015) are widely used to model uncertain information. As a result, many OWA operators combined with fuzzy set theory and evidence theory are presented Yang and Pang (2014) and Reformat and Yager (2007).…”

Section: Introductionmentioning

confidence: 98%

Ordered visibility graph weighted averaging aggregation operator: A methodology based on network analysis

Wang¹,

Mo²,

Sadiq³

et al. 2015

Computers & Industrial Engineering

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“…So one key problem in the EIA is to handle various kinds of forms of uncertainty. Up to now, many methods have been used to deal with uncertainty, such as fuzzy set theory (Zadeh 1965;Wood et al 2007;Zavadskas et al 2014;Ju, Yoo 2014;Jiang et al 2015a), rough set (Pawlak, Skowron 2007;Hu, Lu 2009;Morón et al 2009), uncertain theory (Liu 2014;Ahmadi et al 2015;Deng 2015b), Dempster-Shafer theory of evidence (Dempster 1967;Shafer 1976;Deng 2015a;Jiang et al 2015b), D numbers theory (Deng 2012;Deng et al 2014c) and so on (Zolfani et al 2013;Rabbani et al 2014;Kaplinski et al 2014;Baleženti, T., Baleženti, A. 2014;Akhavan et al 2015;Su et al 2015;Deng et al 2015a;Mardani et al 2015).…”

Section: Introductionmentioning

confidence: 99%

A Modified D Numbers Methodology for Environmental Impact Assessment

Wang

Wei

2017

Technological and Economic Development of Economy

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Abstract. Environmental impact assessment (EIA) is usually evaluated by many factors influenced by various kinds of uncertainty or fuzziness. As a result, the key issues of EIA problem are to represent and deal with the uncertain or fuzzy information. D numbers theory, as the extension of Dempster-Shafer theory of evidence, is a desirable tool that can express uncertainty and fuzziness, both complete and incomplete, quantitative or qualitative. However, some shortcomings do exist in D numbers combination process, the commutative property is not well considered when multiple D numbers are combined. Though some attempts have made to solve this problem, the previous method is not appropriate and convenience as more information about the given evaluations represented by D numbers are needed. In this paper, a data-driven D numbers combination rule is proposed, commutative property is well considered in the proposed method. In the combination process, there does not require any new information except the original D numbers. An illustrative example is provided to demonstrate the effectiveness of the method.

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An improved method to rank generalized fuzzy numbers with different left heights and right heights

Cited by 78 publications

References 20 publications

Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers

Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers

Ordered visibility graph weighted averaging aggregation operator: A methodology based on network analysis

A Modified D Numbers Methodology for Environmental Impact Assessment

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