• DocumentCode
    3643070
  • Title

    Fault modelling using a mixture of conditional Gaussian Transitions

  • Author

    Dejan P. Jovanović;Ross S. McVinish;Philip K. Pollett

  • Author_Institution
    Department of Mathematics, The University of Queensland, 4072 AUSTRALIA
  • fYear
    2011
  • fDate
    6/1/2011 12:00:00 AM
  • Firstpage
    473
  • Lastpage
    478
  • Abstract
    To model a fault that can be caused by more than one source, a mixture of conditional Gaussian transitions is proposed. The conditional means are modelled by recurrent neural networks. An expectation-maximization (EM) algorithm is used to estimate model parameters. By grouping known types of faults it is possible to form a bank of different fault models.
  • Keywords
    "Mathematical model","Markov processes","Recurrent neural networks","Predictive models","Time series analysis","Training","Equations"
  • Publisher
    ieee
  • Conference_Titel
    Control & Automation (MED), 2011 19th Mediterranean Conference on
  • Print_ISBN
    978-1-4577-0124-5
  • Type

    conf

  • DOI
    10.1109/MED.2011.5983194
  • Filename
    5983194
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3643070