On the centroids of fuzzy numbers

Wang, Ying‐Ming; Yang, Jian-Bo; Xu, Dong-Ling; Chin, Kwai‐Sang

doi:10.1016/j.fss.2005.11.006

Cited by 301 publications

(128 citation statements)

References 7 publications

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“…However a survey of literature on the research work focusing on various potential problems and challenges in the face detection and recognition can be found in [20]. Face detection using fuzzy pattern matching method based on skin and hair colour has been prepared by [19] . This method has high detection rate, but it fails, if the hair colour is not black and face regions not elliptic.…”

Section: Existing Workmentioning

confidence: 99%

A New Method For Face Recognition Using Feature Clustering With Fuzzy Parameters

Biswas¹,

Som²

2013

J. Math. Computer Sci.

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In this paper, we have applied Gabor filter for fiducial point localisation. The fiducial points are represented as trapezoidal fuzzy numbers. These fiducial points are then transformed into crisp numbers. The number of fiducial points are then reduced by using a distance formula. The distance of each of these fiducial points are then stored in the database of the system. The same methodology is applied on the input face which is to be matched with the faces available in the database. Then a fuzzy preference relation matrix is obtained .The largest eigen value of this matrix is then determined. Once the largest eigen value is determined the corresponding priority vector can easily be obtained, from which we can easily match the input face with the database. In the broadest sense we have observed we have used the fuzzy mathematics to counter the impreciseness in facial recognition.

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Section: Existing Workmentioning

confidence: 99%

A New Method For Face Recognition Using Feature Clustering With Fuzzy Parameters

Biswas¹,

Som²

2013

J. Math. Computer Sci.

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“…The centroid ranking method from Wang et al [24] (2006) is applied to rank the alternatives and detailed interpretation of centroid equations can be seen in Wang et al' paper. The centroid on x-axis and y-axis can be shown as follows: …”

Section: Obtain Rankingmentioning

confidence: 99%

“…To resolve these problems, this paper suggests applying the ranking approach of centroid from Wang et al [24] (2006) to obtain the Euclidean distance to defuzzify all the final fuzzy evaluation values in order to complete the model. Formulae can be clearly developed to present the ranking procedure.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Consulting Firms Using a Centroid Ranking Approach based Fuzzy MCDM Method

Chu¹

2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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The purpose of this paper is to evaluate and select consulting firms by suggesting a centroid ranking approach based fuzzy multiple criteria decision making (MCDM) method, where ratings of alternatives versus qualitative criteria and the different importance weights among criteria are assessed in linguistic values represented by fuzzy numbers. Criteria are categorized to qualitative, benefit quantitative and cost quantitative ones. Membership functions of the final fuzzy evaluation values can be developed through α-cuts and interval arithmetic. The ranking approach of Euclidean distance based on centroid points is applied to determine the ranking order of all alternatives. Formulas for the ranking procedure can be clearly derived. Finally, a numerical example is used to demonstrate the computational process of the suggested model.

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“…Gajivaradhan and Parthiban analysed one-way ANOVA test using alpha cut interval method for trapezoidal fuzzy numbers [16] and they presented a comparative study of 2-factor ANOVA test under fuzzy environments using various methods [17] Liou and Wang ranked fuzzy numbers with total integral value [14]. Wang et al presented the method for centroid formulae for a generalized fuzzy number [25]. Iuliana Carmen BĂRBĂCIORU dealt with the statistical hypotheses testing using membership function of fuzzy numbers [12].…”

Section: Introductionmentioning

confidence: 99%

Statistical Hypothesis Test in Three Factor ANOVA Model under Fuzzy Environments Using Trapezoidal Fuzzy Numbers

Parthiban¹,

Gajivaradhan²

2016

BMSA

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This paper deals with the problem of three factor ANOVA model (Latin Square Design-LSD) test using Trapezoidal Fuzzy Numbers (tfns.). The proposed test is analysed under various types of trapezoidal fuzzy models such as Alpha Cut Interval, Membership Function, Ranking Function, Total Integral Value and Graded Mean Integration Representation. Finally a comparative view of the conclusions obtained from various test is given. Moreover, two numerical examples having different conclusions have been given for a concrete comparative study.

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On the centroids of fuzzy numbers

Cited by 301 publications

References 7 publications

A New Method For Face Recognition Using Feature Clustering With Fuzzy Parameters

A New Method For Face Recognition Using Feature Clustering With Fuzzy Parameters

Evaluating Consulting Firms Using a Centroid Ranking Approach based Fuzzy MCDM Method

Statistical Hypothesis Test in Three Factor ANOVA Model under Fuzzy Environments Using Trapezoidal Fuzzy Numbers

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