• DocumentCode
    358247
  • Title

    Improved kernel density estimation for clustered data using regularisation and deconvolution

  • Author

    Chen, Q. ; Sandoz, D. ; Wynne, R.J. ; Kruger, U.

  • Author_Institution
    Manchester Univ., UK
  • Volume
    2
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    1410
  • Abstract
    To extract multivariate probability density functions (PDF) from a clustered training data set for condition monitoring purposes, a modified kernel density estimation method is suggested using regularisation and deconvolution techniques. Case studies show that it is a useful pragmatic method for real industrial data
  • Keywords
    condition monitoring; deconvolution; probability; process monitoring; clustered data; condition monitoring; deconvolution; kernel density estimation; multivariate probability density functions; regularisation; Bandwidth; Condition monitoring; Data mining; Deconvolution; Density functional theory; Kernel; Neural networks; Noise level; Statistics; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference, 2000. Proceedings of the 2000
  • Conference_Location
    Chicago, IL
  • ISSN
    0743-1619
  • Print_ISBN
    0-7803-5519-9
  • Type

    conf

  • DOI
    10.1109/ACC.2000.876733
  • Filename
    876733
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=358247