2017
DOI: 10.3390/sym9100198
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A Generalization of Trapezoidal Fuzzy Numbers Based on Modal Interval Theory

Abstract: Abstract:We propose a generalization of trapezoidal fuzzy numbers based on modal interval theory, which we name "modal interval trapezoidal fuzzy numbers". In this generalization, we accept that the alpha cuts associated with a trapezoidal fuzzy number can be modal intervals, also allowing that two interval modalities can be associated with a trapezoidal fuzzy number. In this context, it is difficult to maintain the traditional graphic representation of trapezoidal fuzzy numbers and we must use the interval pl… Show more

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Cited by 7 publications
(9 citation statements)
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“…In this regard, we can point out [23] in a non-life insurance context, [24] to interpret the parameter that quantifies the dependence in a Farlie-Gumbel-Morgestein copula, and [25][26][27][28] in life insurance pricing. Since any interval can be seen as the -cut of a FN, even in the case of improper intervals ( [29]), in this work we consider that is fitted by means of a FN and, more particularly, by a triangular fuzzy number (TFN). So, this paper builds up a framework to model Markovian BMSs that embed the standard case, where the risk parameter is crisp, but also the method developed in [11] that quantifies this parameter as a modal interval.…”
Section: Noveltiesmentioning
confidence: 99%
See 1 more Smart Citation
“…In this regard, we can point out [23] in a non-life insurance context, [24] to interpret the parameter that quantifies the dependence in a Farlie-Gumbel-Morgestein copula, and [25][26][27][28] in life insurance pricing. Since any interval can be seen as the -cut of a FN, even in the case of improper intervals ( [29]), in this work we consider that is fitted by means of a FN and, more particularly, by a triangular fuzzy number (TFN). So, this paper builds up a framework to model Markovian BMSs that embed the standard case, where the risk parameter is crisp, but also the method developed in [11] that quantifies this parameter as a modal interval.…”
Section: Noveltiesmentioning
confidence: 99%
“…Equations (26), (29) and (30) do not give a TFN. However, can be approximated as a TFN simply by using Equation (18).…”
Section: Implementing a Markovian Fuzzy Bonus-malus System Governed Bmentioning
confidence: 99%
“…Definition 1 (see [14]). There are two trapezoidal fuzzy numbers a = a 1 , a 2 , a 3 Definition 2 (see [14]).…”
Section: Trapezoidal Fuzzy Number and Language Evaluation Phrasesmentioning
confidence: 99%
“…Definition 1 (see [14]). There are two trapezoidal fuzzy numbers a = a 1 , a 2 , a 3 Definition 2 (see [14]). There are two trapezoidal fuzzy numbers a = a 1 , a 2 , a 3 , a 4…”
Section: Trapezoidal Fuzzy Number and Language Evaluation Phrasesmentioning
confidence: 99%
“…Such deviations in subjective estimations can lead to fuzziness being inherent in the real-world decision problems (e.g., vagueness and/or impreciseness in the outcomes of a water pollution control sample), neglect of which can cause the solutions of problems deviating greatly from their true values. Fuzzy mathematic programming (FMP), based on fuzzy sets theory, can facilitate the analysis of system associated with uncertainties being derived from vagueness and imprecision [6,7]. FMP is capable of handling decision problems under fuzzy goal and constraints and tackling ambiguous coefficients in the objective function and constraints.…”
Section: Introductionmentioning
confidence: 99%