• DocumentCode
    2343147
  • Title

    Study on dynamic recursive neural network structure and learning algorithm

  • Author

    Tianyun, Shi ; Jia Limin

  • Author_Institution
    Res. Center of Intelligent Control, China Acad. of Railway, Beijing, China
  • Volume
    2
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    813
  • Abstract
    In order to solve the present problem of dynamic recursive neural network such as slow learning speed, low model accuracy and bad application result, several new dynamic recursive network are put forward based on its structure. The approach of neural network automatic design based on the integration of self-adaptive evolutionary strategy and improved backpropagation algorithm is also advanced to realize the rapid evolution of network structure, weights and self feedback parameter in the same time. The actual application of system modeling shows that the advanced dynamic recursive network and learning algorithm is feasible and perfect
  • Keywords
    backpropagation; evolutionary computation; recurrent neural nets; back propagation algorithm; backpropagation algorithm; dynamic recursive neural network structure; learning algorithm; learning speed; model accuracy; network weights; self feedback parameter; self-adaptive evolutionary strategy; Algorithm design and analysis; Intelligent control; Modeling; Neural networks; Neurofeedback; Rail transportation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
  • Conference_Location
    Hefei
  • Print_ISBN
    0-7803-5995-X
  • Type

    conf

  • DOI
    10.1109/WCICA.2000.863342
  • Filename
    863342