• DocumentCode
    1817735
  • Title

    Mathematical justification of recurrent neural networks with long and short-term memories

  • Author

    Lo, James T. ; Bassu, Devasis

  • Author_Institution
    Dept. of Math. & Stat., Maryland Univ., Baltimore, MD, USA
  • Volume
    1
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    364
  • Abstract
    Two theorems are given, that justify the use of multi-layer perceptrons with interconnected neurons (MLPWIN) with long- and short-term memories (LASTMs) for both Lp and risk-sensitive adaptive processing. The benefits of using a MLPWIN with LASTMs include less online computation, no poor local extrema to fall into, and much more timely and better adaptation
  • Keywords
    learning (artificial intelligence); multilayer perceptrons; recurrent neural nets; adaptation; interconnected neurons; long-term memories; risk-sensitive adaptive processing; short-term memories; Adaptive algorithm; Adaptive filters; Information filtering; Information filters; Mathematics; Multilayer perceptrons; Neurons; Nonlinear filters; Recurrent neural networks; Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1999. IJCNN '99. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-5529-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.1999.831520
  • Filename
    831520
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1817735