• DocumentCode
    289900
  • Title

    Introduction of neural networks in the decentralized detection problems

  • Author

    Duflos, E. ; Boyer, M.-P. ; Coquard, Y. ; Vanheeghe, P.

  • Author_Institution
    Dept. Signaux et Syst., Inst. Superieur d´´Electron. du Nord, Lille, France
  • fYear
    1993
  • fDate
    17-20 Oct 1993
  • Firstpage
    650
  • Abstract
    The problem of decentralized detection appears when various sensors are used simultaneously to collect observations from multiple points apart from each other. In this case, some difficulties such as transmission errors, very high transmission rates or memory requirement may occur when a centralized detection is used. To override these limitations detection structures involving two processing levels have been introduced: the decentralized detection. Unfortunately the solutions proposed to the decentralized problem require many calculations. Basing their approach on the similarity between detection problems and neural networks the authors propose a neural approach to solve the decentralized detection problem with fewer calculations
  • Keywords
    neural nets; sensor fusion; decentralized detection problems; memory requirement; neural networks; transmission errors; two-level processing; very high transmission rates; Backpropagation algorithms; Conference proceedings; Cybernetics; Equations; Explosions; Filters; Intelligent networks; Neural networks; Sensor fusion; White noise;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 1993. 'Systems Engineering in the Service of Humans', Conference Proceedings., International Conference on
  • Conference_Location
    Le Touquet
  • Print_ISBN
    0-7803-0911-1
  • Type

    conf

  • DOI
    10.1109/ICSMC.1993.385090
  • Filename
    385090