Attribute Control Chart Construction Based on Fuzzy Score Number

Hou, Shiwang; Wang, Hui; Feng, Shunshan

doi:10.3390/sym8120139

Cited by 14 publications

(12 citation statements)

References 20 publications

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“…Reference [19] studied the fuzzy np control chart. Reference [20] presented the control chart for the fuzzy score number. Reference [21] presented the algorithm for the control chart under the fuzzy logic.…”

Section: Complexitymentioning

confidence: 99%

Design of a Control Chart Based on COM‐Poisson Distribution for the Uncertainty Environment

Aslam

AL-Marshadi

2019

Complexity

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This paper will introduce the neutrosophic COM-Poisson (NCOM-Poisson) distribution. Then, the design of the attribute control chart using the NCOM-Poisson distribution is given. The structure of the control chart under the neutrosophic statistical interval method will be given. The algorithm to determine the average run length under neutrosophic statistical interval system will be given. The performance of the proposed control chart is compared with the chart based on classical statistics in terms of neutrosophic average run length (NARL). A simulation study and a real example are also added. From the comparison of the proposed control chart with the existing chart, it is concluded that the proposed control chart is more efficient in detecting a shift in the process. Therefore, the proposed control chart will be helpful in minimizing the defective product. In addition, the proposed control chart is more adequate and effective to apply in uncertainty environment.

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Section: Complexitymentioning

confidence: 99%

Design of a Control Chart Based on COM‐Poisson Distribution for the Uncertainty Environment

Aslam

AL-Marshadi

2019

Complexity

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“…Also, they developed a new fuzzy c-control chart with WPM and WIVPM. Hou et al (2016) proposed a necessity and possibility measurement rules for the fuzzy control chart in their paper.…”

Section: Introductionmentioning

confidence: 99%

Interval Type-2 Fuzzy c-Control Charts: An Application in a Food Company

Şentürk¹,

Antuchevičienė²

2017

Informatica

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Many papers exist on ordinary fuzzy control charts in literature in order to consider the vagueness and uncertainty in observation data. These are on both variable and attribute control charts. Several extensions of fuzzy sets have appeared in literature since ordinary fuzzy sets emerged. Type-2 fuzzy sets are one of these extensions. Type-2 fuzzy sets take into account the imprecision of membership functions in three dimensions. The aim of this paper is to develop interval type-2 fuzzy control charts for number of nonconformities, briefly c-control charts. In this paper, the theoretical structure of interval type-2 fuzzy c-control charts is proposed for the first time and the application is implemented in a food company.

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“…The solution for X is: 15,10] , [9,14]) − dual ( [2,9] , [6,7]) = ( [13,1] , [3,7]) , which is an MITFN P I (see Figure 6). The transition modality value for X is α 0 X = 3 4 = 0.75 and the transition modality value for B is α 0 B = 1 2 = 0.5; thus, the interpretation of the calculus A + X = B is:…”

Section: Corollary 2 Under the Above Conditions Of Theoremmentioning

confidence: 99%

“…Next, let us consider the fuzzy equation A + X = B, where A is the MITFN P P , A = ( [2,9] , [6,7]) and B is the MITFN P I , B = ( [15,10] , [9,14]) . The solution for X is: 15,10] , [9,14]) − dual ( [2,9] , [6,7]) = ( [13,1] , [3,7]) , which is an MITFN P I (see Figure 6).…”

Section: Corollary 2 Under the Above Conditions Of Theoremmentioning

confidence: 99%

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A Generalization of Trapezoidal Fuzzy Numbers Based on Modal Interval Theory

Jorba

Adillon

2017

Symmetry

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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 plane in order to represent our modal interval trapezoidal fuzzy numbers graphically. Using this representation, we can correctly reflect the modality of the alpha cuts. We define some concepts from modal interval analysis and we study some of the related properties and structures, proving, among other things, that the inclusion relation provides a lattice structure on this set. We will also provide a semantic interpretation deduced from the modal interval extensions of real continuous functions and the semantic modal interval theorem. The application of modal intervals in the field of fuzzy numbers also provides a new perspective on and new applications of fuzzy numbers.

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Attribute Control Chart Construction Based on Fuzzy Score Number

Cited by 14 publications

References 20 publications

Design of a Control Chart Based on COM‐Poisson Distribution for the Uncertainty Environment

Design of a Control Chart Based on COM‐Poisson Distribution for the Uncertainty Environment

Interval Type-2 Fuzzy c-Control Charts: An Application in a Food Company

A Generalization of Trapezoidal Fuzzy Numbers Based on Modal Interval Theory

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