• DocumentCode
    2206959
  • Title

    Contribution of belief functions to hidden markov models with an application to fault diagnosis

  • Author

    Ramasso, Emmanuel

  • Author_Institution
    Autom. Control & Micro-Mechatron. Syst. Dept., FEMTO-ST Inst., Besancon, France
  • fYear
    2009
  • fDate
    1-4 Sept. 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Evidence-theoretic propagations of temporal belief functions are proposed to deal with possibly dependent observations and for partially supervised learning of HMM. Solutions are formulated in Transferable Belief Model framework and experiments concern a diagnosis problem.
  • Keywords
    belief networks; fault diagnosis; hidden Markov models; learning (artificial intelligence); HMM; evidence-theoretic propagations; fault diagnosis; hidden Markov models; partially supervised learning; temporal belief functions; transferable belief model; Algorithm design and analysis; Automatic control; Bayesian methods; Data analysis; Fault diagnosis; Hidden Markov models; Inference algorithms; Possibility theory; Supervised learning; Uncertainty; Evidential Hidden Markov Models; Partially Supervised Learning; State Sequence Recognition; System´s Health Assessment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE International Workshop on
  • Conference_Location
    Grenoble
  • Print_ISBN
    978-1-4244-4947-7
  • Electronic_ISBN
    978-1-4244-4948-4
  • Type

    conf

  • DOI
    10.1109/MLSP.2009.5306209
  • Filename
    5306209
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2206959