• DocumentCode
    1608344
  • Title

    Using the particle swarm optimization technique to train a recurrent neural model

  • Author

    Salerno, John

  • Author_Institution
    Rome Lab., NY, USA
  • fYear
    1997
  • Firstpage
    45
  • Lastpage
    49
  • Abstract
    We discuss the results of implementing an evolutionary learning technique entitled particle swarm optimization as described by Kennedy and Eberhart (1995). We present a number of results using this technique on a number of neural model architectures using the XOR problem and then conclude by applying it to a real problem-parsing natural language phrases
  • Keywords
    feedforward neural nets; genetic algorithms; grammars; learning (artificial intelligence); multilayer perceptrons; natural languages; neural net architecture; optimisation; recurrent neural nets; XOR problem; evolutionary learning technique; multilayered feedforward network; natural language phrase parsing; neural model architectures; particle swarm optimization technique; recurrent neural model training; Backpropagation algorithms; Convergence; Feeds; Laboratories; Natural languages; Particle swarm optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 1997. Proceedings., Ninth IEEE International Conference on
  • Conference_Location
    Newport Beach, CA
  • ISSN
    1082-3409
  • Print_ISBN
    0-8186-8203-5
  • Type

    conf

  • DOI
    10.1109/TAI.1997.632235
  • Filename
    632235
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1608344