Centroid of polygonal fuzzy sets

Naimi, Mohammad; Tahayori, Hooman

doi:10.1016/j.asoc.2020.106519

Cited by 10 publications

(3 citation statements)

References 34 publications

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“…The result greatly depends on the method we choose for the defuzzification which influences the speed and the accuracy. This study employs the centroid method which is found to be more efficient [29,30] compared with the other alternative defuzzification methods of Mean of maximum and Weighted average. Centroid method determines the centre of area of fuzzy set and returns the corresponding crisp value quickly and precisely.…”

Section: Methodsmentioning

confidence: 99%

Simulation of Optimal Mix of Sio2-Tio2-Al2o3 Nano Additives for the Minimal Wear and Coefficient of Friction of Lubricant Using Fuzzy Logic

2023

Jurnal Teknologi

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Nano-particles of oxides of various metals are typically added with the base lubricants for enhancing the tribological properties, especially the wear and coefficient of friction of the lubricants. However, it is required to determine the optimum proportion of mix of the nano-additives in order to obtain the desirable characteristics gained through the addition of various oxides of metals mixed with the lubricants. This paper deals with the application of fuzzy logic to simulate the various proportions of a mix of three nano-additives of SiO2, Al2O3 and TiO2 with the corresponding predicted values of tribological characteristics of wear and coefficient of friction. The limited number of outcomes obtained from a full factorial design of experiments carried out with these three nano-additives at three different levels of mixing, are used as inputs to the fuzzy logic simulation. This approach facilitates simulating as many combinations of nano-additives as possible with the base lubricant oil along with the predicted output of characteristics of interest, which otherwise will be cumbersome to carry out the experiments for all the combinations of mix of additives.

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Section: Methodsmentioning

confidence: 99%

Simulation of Optimal Mix of Sio2-Tio2-Al2o3 Nano Additives for the Minimal Wear and Coefficient of Friction of Lubricant Using Fuzzy Logic

2023

Jurnal Teknologi

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“…Fuzzy set theory [51] was primarily developed to formulate imprecise information in order to make it a logical conclusion for any decision-making problem, in which the imprecise linguistic terms, such as poor, high, good, low, etc., are translated into numeric values using fuzzy membership functions [52]. In addition, modeling imprecise concepts in fuzzy sets performs through allocating a membership degree to the elements of the set [53].…”

Section: Uncertainty and Strategic Analysismentioning

confidence: 99%

The Grey Ten-Element Analysis Method: A Novel Strategic Analysis Tool

2022

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In this paper, a new strategic analysis method is introduced, called the ten-element analysis (TEA) method to determine the firm’s strategic position in the market. The new method is grounded on the computation of the reflections of the external factors on the firm’s internal factors through the changes of the values of the internal factors throughout the time when a lack of complete information regarding the environmental factors exists. The TEA method takes ten effective key elements of the firm into account and investigates their changes through a maximum of nine periods and a minimum of two periods. To conduct the model, the paper is mainly focused on four main rubrics, including the detection of the reflection of the firm’s environmental factors on the internal factors, deriving the strategic position of the firm from the reflections, the capability of the existing strategic models in determining the strategic position from the reflections in presence of uncertainty and incomplete information of the external factors. The method is applied to a dairy company in order to find its strategic position in the market. The results showed that the output of the TEA method and SWOT analysis is similar which makes the new method reliable to employ. The TEA method is developed under the grey environment to harness the uncertainty where a new grey comparison method is introduced to compare the grey numbers.

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“…Scholars have done a lot of research on various properties of fuzzy sets, including the operations between fuzzy sets, the centroid and similarity of fuzzy sets (Chi and Vincent, 2018;Chen, 2019;Naimi, 2020), and the entire fuzzy logic system has fully developed (Zhao, 2019). The preliminary data input fuzzy rules include Mamdani fuzzy rules and TSK fuzzy rules (Mamdani, 1974;Takagi and Sugeno, 1985); however, there are few researches on the ranking of fuzzy numbers.…”

Section: Introductionmentioning

confidence: 99%