• DocumentCode
    2296895
  • Title

    Dynamic subgrouping in RTRL provides a faster O(N2) algorithm

  • Author

    Euliano, Neil R. ; Principe, Jose C.

  • Author_Institution
    Comput. Neuro Eng. Lab., Florida Univ., Gainesville, FL, USA
  • Volume
    6
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    3418
  • Abstract
    Static grouping of processing elements (PEs) has been proposed to reduce the computational complexity of real time recurrent learning (RTRL) from O(n4) to O(n2), but performance suffers. This paper proposes a dynamic subgrouping of PEs estimated from a local approximation of the π matrix based on temporal Hebbian of sensitivities during training. The method is O(n2) and leads to better performance
  • Keywords
    Hebbian learning; approximation theory; computational complexity; recurrent neural nets; π matrix; RTRL; computational complexity reduction; dynamic subgrouping; first-order approximation; local approximation; processing elements; real time recurrent learning; sensitivity matrix; temporal Hebbian learning; training; Computational complexity; Computer networks; Feedforward neural networks; History; Laboratories; Neural engineering; Neural networks; Recurrent neural networks; Sun; System identification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on
  • Conference_Location
    Istanbul
  • ISSN
    1520-6149
  • Print_ISBN
    0-7803-6293-4
  • Type

    conf

  • DOI
    10.1109/ICASSP.2000.860135
  • Filename
    860135
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2296895