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Tekst fra siroos - English

  • spc

    • 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.

VÆR SÅ SNILL, HJELP TIL MED Å RETTE HVER SETNING! - English

  • Tittel
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    • 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.
      Stem nå!
    • A X-MR control chart is a binary control chart on which average values of processes and¶moving ranges between observations are used to discover the variability in thesaid¶processes.
      Terry_PTerry_P profile picture Kommentarer
      Stem nå!
    • LEGG TIL EN NY RETTELSE! - Setning 3LEGG TIL EN NY RETTELSE! - Setning 3
  • Setning 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.
      Stem nå!
    • LEGG TIL EN NY RETTELSE! - Setning 4LEGG TIL EN NY RETTELSE! - Setning 4
  • Setning 5
  • Setning 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.
      Stem nå!
    • LEGG TIL EN NY RETTELSE! - Setning 6LEGG TIL EN NY RETTELSE! - Setning 6
  • Setning 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.
      Stem nå!
    • LEGG TIL EN NY RETTELSE! - Setning 7LEGG TIL EN NY RETTELSE! - Setning 7
  • Setning 8
    • In fact, by using defined Dp,q- distance between fuzzy numbers, their variance and covariance are obtained, then the autocorrelation coefficient is calculated.
      Stem nå!
    • LEGG TIL EN NY RETTELSE! - Setning 8LEGG TIL EN NY RETTELSE! - Setning 8
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  • Setning 10