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  • A control chart is a tool for studying of a production process in order to be controlled.
  • Being in control of the process means standing of both central and dispersion parameters of surveyed attribute on their target values.
    • X-MR control chart is a binary control chart on which average values of process and moving range between observations are used to discover the variability in the process.
    • In ordinary control chart, the data are crisp values but sometimes, the data are generated as vague and uncertain values because of some of environmental conditions and other factors.
    • In such cases, fuzzy sets theory is a useful tool for analyzing data.
    • Sometime, assumption of independence between observations cannot be accepted because probability of false warning will increase if the data are autocorrelated and their correlation is ignored.
  • In this article, attempts are made to discuss the construction of fuzzy control charts for autocorrelated fuzzy observations and employment of ranking method for finding out whether the observations are in or out of control.
  • In fact, by using defined Dp,q- distance between fuzzy numbers, their variance and covariance are obtained, then the autocorrelation coefficient is calculated.
  • The autocorrelation coefficient is used in order to modify the limit of control chart.
  • By using Dp,q-distance we present a new approach for designing of the control charts.

PROSÍM, POMOŽTE OPRAVIT KAŽDOU VĚTU! - English

  • Titul
  • Sentence 1
  • Sentence 2
    • Being in control of the process means standing of both central and dispersion parameters of surveyed attribute on their target values.
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    • PŘIDAT NOVOU OPRAVU! - Sentence 2PŘIDAT NOVOU OPRAVU! - Sentence 2
  • Sentence 3
    • X-MR control chart is a binary control chart on which average values of process and moving range between observations are used to discover the variability in the process.
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    • An X-MR control chart is a binary control chart on which average values of process and moving range between observations are used to discover the variability in the process.
      genzaimugenzaimu profile picture Komentáře
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    • PŘIDAT NOVOU OPRAVU! - Sentence 3PŘIDAT NOVOU OPRAVU! - Sentence 3
  • Sentence 4
    • In ordinary control chart, the data are crisp values but sometimes, the data are generated as vague and uncertain values because of some of environmental conditions and other factors.
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    • In an ordinary control chart, the data arehas crisp values, but sometimes, the data¶are is generated as vague and uncertain values because of some of environmental conditions and other factors.
      genzaimugenzaimu profile picture Komentáře
      Vote now!
    • PŘIDAT NOVOU OPRAVU! - Sentence 4PŘIDAT NOVOU OPRAVU! - Sentence 4
  • Sentence 5
  • Sentence 6
    • Sometime, assumption of independence between observations cannot be accepted because probability of false warning will increase if the data are autocorrelated and their correlation is ignored.
      Vote now!
    • Sometimes, assumption of independence between observations cannot be accepted because probability of false warning will increase if the data areis autocorrelated and their correlation is ignored.
      genzaimugenzaimu profile picture Komentáře
      Vote now!
    • PŘIDAT NOVOU OPRAVU! - Sentence 6PŘIDAT NOVOU OPRAVU! - Sentence 6
  • Sentence 7
    • In this article, attempts are made to discuss the construction of fuzzy control charts for autocorrelated fuzzy observations and employment of ranking method for finding out whether the observations are in or out of control.
      Vote now!
    • PŘIDAT NOVOU OPRAVU! - Sentence 7PŘIDAT NOVOU OPRAVU! - Sentence 7
  • Sentence 8
    • In fact, by using defined Dp,q- distance between fuzzy numbers, their variance and covariance are obtained, then the autocorrelation coefficient is calculated.
      Vote now!
    • PŘIDAT NOVOU OPRAVU! - Sentence 8PŘIDAT NOVOU OPRAVU! - Sentence 8
  • Sentence 9
  • Sentence 10