IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2020i1p10-d466720.html
   My bibliography  Save this article

Stable and Unstable Pattern Recognition Using D 2 and SVM: A Multivariate Approach

Author

Listed:
  • Pamela Chiñas-Sanchez

    (Tecnologico Nacional de Mexico, Instituto Tecnologico de Saltillo, Saltillo 25280, Mexico)

  • Ismael Lopez-Juarez

    (Centre for Research and Advanced Studies (CINVESTAV), Ramos Arizpe 25900, Mexico
    Current address: Ind Metalurgica 1062, P Ind Saltillo-Ramos Arizpe, Ramos Arizpe, Coahuila 25900, Mexico.)

  • Jose Antonio Vazquez-Lopez

    (Tecnologico Nacional de Mexico, Instituto Tecnologico de Celaya, Celaya 38010, Mexico)

  • Abdelkader El Kamel

    (Ecole Centrale de Lille, 59650 Villeneuve d’ascq, France)

  • Jose Luis Navarro-Gonzalez

    (IJ Robotics SA de CV, Saltillo 25000, Mexico)

Abstract

Control charts are used to visually identify the signals that define the behavior of industrial processes in univariate cases. However, whenever the statistical quality of more than one critical variable needs to be monitored simultaneously, the procedure becomes much more complicated. This paper presents a methodology on multivariate pattern recognition using the Mahalanobis distance ( D 2 ) and the Support Vector Machine (SVM) technique to recognise two multivariate patterns. The relevance of the study lies in the monitoring of the variables while considering the correlation between them and the effects of interchangeably using a stable multivariate case against an unstable pattern that results in recognition rates up to 91.6 % .

Suggested Citation

  • Pamela Chiñas-Sanchez & Ismael Lopez-Juarez & Jose Antonio Vazquez-Lopez & Abdelkader El Kamel & Jose Luis Navarro-Gonzalez, 2020. "Stable and Unstable Pattern Recognition Using D 2 and SVM: A Multivariate Approach," Mathematics, MDPI, vol. 9(1), pages 1-12, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2020:i:1:p:10-:d:466720
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/1/10/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/1/10/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yanjing Ou & Nan Chen & Michael B.C. Khoo, 2015. "An efficient multivariate control charting mechanism based on SPRT," International Journal of Production Research, Taylor & Francis Journals, vol. 53(7), pages 1937-1949, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2020:i:1:p:10-:d:466720. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.