• DocumentCode
    489397
  • Title

    Linear System State Estimation: A Neurocomputing Approach

  • Author

    Sun, Q. ; Alouani, A.T. ; Rice, T.R. ; Gray, J.E.

  • Author_Institution
    Electrical Engineering Department, Tennessee Technological University, Cookeville, TN 38505
  • fYear
    1992
  • fDate
    24-26 June 1992
  • Firstpage
    550
  • Lastpage
    554
  • Abstract
    A neurocomputing approach is developed in this paper to solve the problem of state estimation for linear dynamical systems. Dynamic optimization techniques are used to develop the adaptation laws for assigning the weights of a Hopfield net. Simulation results show that the new approach performs similar to Kalman filter, and outperforms it for some special situations. The new approach is very attractive for the real-time implementation of a state estimator for large scale systems.
  • Keywords
    Error analysis; Filtering theory; Filters; Hopfield neural networks; Large-scale systems; Linear systems; Real time systems; State estimation; Sun; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference, 1992
  • Conference_Location
    Chicago, IL, USA
  • Print_ISBN
    0-7803-0210-9
  • Type

    conf

  • Filename
    4792126
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=489397