• DocumentCode
    2049189
  • Title

    Hill climbing in recurrent neural networks for learning the an bncn language

  • Author

    Chalup, Stcphau ; Blair, Alan D.

  • Author_Institution
    Fac. of Inf. Technol., Queensland Univ. of Technol., Brisbane, Qld., Australia
  • Volume
    2
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    508
  • Abstract
    A simple recurrent neural network is trained on a one-step look ahead prediction task for symbol sequences of the context-sensitive a nbncn language. Using an evolutionary hill climbing strategy for incremental learning the network learns to predict sequences of strings up to depth n=12. Experiments and the algorithms used are described. The activation of the hidden units of the trained network is displayed in a 3D graph and analysed
  • Keywords
    context-sensitive languages; learning (artificial intelligence); recurrent neural nets; sequences; 3D graph; context-sensitive anbncn language learning; evolutionary hill climbing strategy; hidden unit activation; incremental learning; one-step look ahead prediction task; recurrent neural networks; string sequence prediction; symbol sequences; trained network; Australia; Computer networks; Intelligent networks; Neural networks; Recurrent neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on
  • Conference_Location
    Perth, WA
  • Print_ISBN
    0-7803-5871-6
  • Type

    conf

  • DOI
    10.1109/ICONIP.1999.845646
  • Filename
    845646
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2049189