Correlation Coefficient Between Fuzzy Numbers Based On Central Interval

Saneifard, R.; Rasoul, Saneifard

doi:10.5899/2012/jfsva-00108

Cited by 7 publications

(7 citation statements)

References 14 publications

(20 reference statements)

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“…Various theories exist for describing uncertainty in the modelling of real phenomena and the most popular one is fuzzy set theory [9]. In this paper we applied the concept of the interval-valued possibilistic mean of fuzzy number [10][11][12].…”

Section: Basic Concepts Of Fuzzy Set Theorymentioning

confidence: 99%

Evaluation of Underground Zinc Mine Investment Based on Fuzzy-Interval Grey System Theory and Geometric Brownian Motion

Gligorić

Kričak

Beljić

et al. 2014

Journal of Applied Mathematics

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Underground mine projects are often associated with diverse sources of uncertainties. Having the ability to plan for these uncertainties plays a key role in the process of project evaluation and is increasingly recognized as critical to mining project success. To make the best decision, based on the information available, it is necessary to develop an adequate model incorporating the uncertainty of the input parameters. The model is developed on the basis of full discounted cash flow analysis of an underground zinc mine project. The relationships between input variables and economic outcomes are complex and often nonlinear. Fuzzyinterval grey system theory is used to forecast zinc metal prices while geometric Brownian motion is used to forecast operating costs over the time frame of the project. To quantify the uncertainty in the parameters within a project, such as capital investment, ore grade, mill recovery, metal content of concentrate, and discount rate, we have applied the concept of interval numbers. The final decision related to project acceptance is based on the net present value of the cash flows generated by the simulation over the time project horizon.

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Section: Basic Concepts Of Fuzzy Set Theorymentioning

confidence: 99%

Evaluation of Underground Zinc Mine Investment Based on Fuzzy-Interval Grey System Theory and Geometric Brownian Motion

Gligorić

Kričak

Beljić

et al. 2014

Journal of Applied Mathematics

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“…We assume that both the importance of the criteria and values of the criteria and also the feedback between the criteria are given by the fuzzy weights calculated from the corresponding pair-wise comparison matrices with trapezoidal fuzzy elements. Let ̃ be a reciprocal pair-wise comparison matrix with fuzzy elements (9). Following [2,7], we shall calculate fuzzy vectors of trapezoidal fuzzy weights ̃ ̃ ̃ , such that a special distance between ̃ and weights ̃ ̃ ̃ is minimized.…”

Section: Algorithmmentioning

confidence: 99%

“…However, perfect consistency rarely occurs in practice. In the AHO the pair-wise comparisons is a judgment matrix are considered to be adequately consistent if the corresponding consistency ratio ( ) is less than 10٪ [3,9]. The coefficient is calculated as follows.…”

Section: Inconsistencymentioning

confidence: 99%

Development of Fuzzy Multi Criteria Decision by Generalized Trapezoidal Pair-Wise Comparison Matrices

Saneifard¹,

Sariojaghi²,

Khodapanahandeh³

2013

Journal of Soft Computing and Applications

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One problem is that managers and experts to decide how to choose the best option among several options according to the decision criteria.In this paper we propose a new Analytic hierarchy process is a decision support tool which can be used to solve complex decision problems and also a new multi criteria decision making involves a series of techniques that allows a range of criteria related a topic in a pair-wise comparison matrices are rating and weighting, then ranked. In this paper a special SW Microsoft Excel add-in named VISIO and MATLB were use.

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“…In general, for fuzzy data, the correlation coefficient obtained through the Extension Principle is fuzzy as well (Ni and Cheung 2003). Different definitions of fuzzy correlation coefficient have been introduced, some of which result in the possible values of the correlation coefficient within [0,1] (Bustince and Burillo 1995) as opposed to within [-1,1] as defined in (Saneifard and Saneifard 2012) and (Liu and Kao 2002). The latter is the same definition as in standard case and also adopted here.…”

Section: Introductionmentioning

confidence: 99%

Generalized Point Estimators for Fuzzy Multivariate Data

Sunanta

2018

AJS

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Data analysis methods are necessary tools in evaluating and better understanding the information of interest. However, there are limitations in applying standard statistical methods to specific data analyses. The data obtained from different sources are often clouded by imprecision and uncertainty. To overcome this problem, data analysis methods have to be generalized to capture the data uncertainty through statististical methods for fuzzy data. The existing methods are based on the extension principle or require other generalized procedures, such as the calculation of statistics, the extimation of parameters, and the construction of fuzzy confidence regions. The development of these methods to evaluate univariate data has been flourished. However, to solve complex real-world problems, these methods have to be extended and generalized to handle multivariate fuzzy data. In this research, the methods of generalized point estimators, i.e. sample mean, variance-covariance, and correlation coefficient, are extended for the multivariate case through concepts of fuzzy vector and combined fuzzy sample.

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Correlation Coefficient Between Fuzzy Numbers Based On Central Interval

Cited by 7 publications

References 14 publications

Evaluation of Underground Zinc Mine Investment Based on Fuzzy-Interval Grey System Theory and Geometric Brownian Motion

Evaluation of Underground Zinc Mine Investment Based on Fuzzy-Interval Grey System Theory and Geometric Brownian Motion

Development of Fuzzy Multi Criteria Decision by Generalized Trapezoidal Pair-Wise Comparison Matrices

Generalized Point Estimators for Fuzzy Multivariate Data

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