An improved similarity measure for generalized fuzzy numbers and its application to fuzzy risk analysis

Khorshidi, Hadi Akbarzadeh; Nikfalazar, Sanaz

doi:10.1016/j.asoc.2016.10.020

Cited by 61 publications

(43 citation statements)

References 16 publications

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“…Although the above equation is improved on previous research, this method still has two significant defects; therefore, Khorshidi & Nikfalazar [ 65 ] proposed the following improved calculation method:

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Section: Calculation Of Similarity Measurementioning

confidence: 99%

Fuzzy risk assessment of a deeply buried tunnel under incomplete information

et al. 2018

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Risk assessment has always been an important part of safety risk research in tunnel and underground engineering. Owing to the characteristics of tunnel construction, to achieve an expected risk control effect, it is necessary to carry out accurate risk assessment research according to the risk assessment concept based on the entire tunnel construction process. At present, because of the frequent occurrences of safety accidents, a variety of risk assessment models have been proposed for different tunnel projects such as subways and railway tunnels, which can be roughly classified into two types: probability-based and fuzzy set theories. However, the existing models may be more suitable for the construction stage, and the design stage lacks a reliable and practical fuzzy risk assessment method. Therefore, based on fuzzy set theory and similarity measure theory, a risk assessment model is proposed to adapt to the characteristics that the risk information is difficult to quantify the fuzziness in the design phase. Firstly, new ideas of fuzzy risk analysis are proposed to overcome deficiencies in existing methods; secondly, a new similarity measure is constructed; then fusing multi-source fuzzy information based on evidence theory, the relationship between similarity measure and mass function is established. Finally, the new method is applied to the Yuelongmen tunnel. Results show that the concept of risk control and the risk assessment model are feasible.

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Section: Calculation Of Similarity Measurementioning

confidence: 99%

Fuzzy risk assessment of a deeply buried tunnel under incomplete information

et al. 2018

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“…Wagner et al [7] propose a similarity measure for Type-1 Fuzzy Numbers which utilizes the Jaccard similarity coefficient and is applicable to IAA Fuzzy Numbers [7,8]. In contrast to [7], Gunn et al [9] propose a similarity measure for IAA Fuzzy Numbers that utilizes a collection of attributes as features, along with each weight of said feature calculated by Principal Component Analysis (PCA); this alternative technique was inspired by Khorshidi and Nikfalazar's similarity measure for Generalized Fuzzy Numbers in [10]. Note that the proposals of [9] are in their current state only applicable to Type-1 fuzzy numbers.…”

Section: Introductionmentioning

confidence: 99%

“…Note that the proposals of [9] are in their current state only applicable to Type-1 fuzzy numbers. Various types of fuzzy numbers have seen proposals for both similarity measures and ranking methods [10,11,12,13], which has provided opportunities for their further practical application. However, previous to this study, there has not been a method of ranking for IAA Fuzzy Numbers formally proposed within the surrounding literature.…”

Section: Introductionmentioning

confidence: 99%

Methods of ranking for aggregated fuzzy numbers from interval-valued data

Gunn

Khorshidi

Aickelin

2020

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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This paper primarily presents two methods of ranking aggregated fuzzy numbers from intervals using the Interval Agreement Approach (IAA). The two proposed ranking methods within this study contain the combination and application of previously proposed similarity measures, along with attributes novel to that of aggregated fuzzy numbers from interval-valued data. The shortcomings of previous measures, along with the improvements of the proposed methods, are illustrated using both a synthetic and real-world application. The real-world application regards the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm, modified to include both the previous and newly proposed methods.

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“…Various fuzzy similarity measures which take into consideration factors like distance, center of gravity, spread, Jaccard index, Dice similarity index, geometric mean and geometric shape characteristics like height, area and parameter have been introduced in the literature [4][5][6][7][8][9][10]. Recently [11] introduced a generalized similarity measure that can measure most types of fuzzy numbers, meanwhile [12] proposed a similarity with multiple features to overcome shortcomings of some existing similarity measures.…”

Section: Introductionmentioning

confidence: 99%

“…Ref [14] introduced and utilized similarity measure in matching fingerprint image. Risk analysis problems have been solved by [8,12,15]. A forecasting problem has been studied by [16] where a combined method of fuzzy time series and similarity measure is used to predict the future stock values.…”

Section: Introductionmentioning

confidence: 99%

Extended FTOPSIS with Distance and Set Theoretic-Based Similarity Measure

Sulaiman¹,

Mohamad²,

Shariff³

et al. 2018

IJEECS

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Comparing fuzzy numbers is an essential process in deducing the output of many fuzzy decision making methods. One of the comparison methods commonly used is by using similarity measure. The main advantage of the similarity measure over other approaches is its ability to minimize the loss of information in the computational process. Several similarity measures have been applied effectively in fuzzy decision making methods. In this paper, a new similarity measure based on the geometric distance, the center of gravity, Hausdorf distance and the set theoretic similarity formula known as the Dice similarity index are incorporated into the Extended Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method particularly in calculating the closeness coefficients. This similarity measure is in favor of others as it is able to discriminate two similar shape fuzzy numbers effectively with two different locations. A validation process is carried out by implementing the proposed procedure of the Extended FTOPSIS with the new similarity measure in solving a supplier selection problem and the ranking outcome is then compared with the Extended FTOPSIS with other existing similarity measure. The result shows that the Extended FTOPSIS with the proposed similarity measure gives a consistent result without reducing any information in the computational process.

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An improved similarity measure for generalized fuzzy numbers and its application to fuzzy risk analysis

Cited by 61 publications

References 16 publications

Fuzzy risk assessment of a deeply buried tunnel under incomplete information

Fuzzy risk assessment of a deeply buried tunnel under incomplete information

Methods of ranking for aggregated fuzzy numbers from interval-valued data

Extended FTOPSIS with Distance and Set Theoretic-Based Similarity Measure

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