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Type 2 Fuzzy Control Charts Using Likelihood and Deffuzzification Methods

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Advances in Fuzzy Logic and Technology 2017 (EUSFLAT 2017, IWIFSGN 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 643))

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Abstract

Besides control charts are used in many fields, they are important because the process gives information about the product’s situation. Thanks to control charts, necessary precautions are taken by noticing abnormal and normal situations of process and/or product. It is considered that at this point the most important and critical thing is that there will be loss of information about the expert opinions. It can be said that this situation is more common especially for the qualitative data. To prevent losses of data like this and so on and to transform linguistic expressions into crisp data, it is needed to take advantage of fuzzy logic that is commonly used recently. Although some studies about creating control charts by fuzzy sets have been done recently, all of them are done only by using type 1 fuzzy sets. However, it is known that much of the data used in daily life cannot be expressed by type 1 fuzzy number. Some data may be more suitable for type 2 fuzzy numbers. In this study, type 2 fuzzy control charts are obtained by using the methods of defuzzification and likelihood. The results are compared with the classical control charts This study aims to use type 2 fuzzy sets in control charts as a new approach.

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Author information

Authors and Affiliations

  1. Eskisehir Osmangazi University, Eskisehir, Turkey

    Hatice Ercan Teksen & Ahmet Sermet Anagün

Authors
  1. Hatice Ercan Teksen
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  2. Ahmet Sermet Anagün
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Correspondence to Hatice Ercan Teksen .

Editor information

Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Eulalia Szmidt

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Slawomir Zadrożny

  4. Institute of Biophysics and Biomedical Engineering, Department of Bioinformatics and Mathematical Modelling, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Krassimir T. Atanassov

  5. WIT - Warsaw School of Information Technology, Warsaw, Poland

    Maciej Krawczak

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Teksen, H.E., Anagün, A.S. (2018). Type 2 Fuzzy Control Charts Using Likelihood and Deffuzzification Methods. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-319-66827-7_37

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  • DOI: https://doi.org/10.1007/978-3-319-66827-7_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66826-0

  • Online ISBN: 978-3-319-66827-7

  • eBook Packages: EngineeringEngineering (R0)

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