• DocumentCode
    2495256
  • Title

    A BPGAD neural network algorithm and its efficiency analysis

  • Author

    Ni, Yuanping

  • Author_Institution
    Sch. of Inf. & Autom., Kunming Univ. of Sci. & Technol., Kunming
  • fYear
    2008
  • fDate
    25-27 June 2008
  • Firstpage
    6958
  • Lastpage
    6962
  • Abstract
    In order to enhance the learning efficiency of back propagation (BP) neural network, the studies developed an improving BP algorithm based on the gradient ascend and descend (BPGAD), discussed the conditions for the convergence of BPGAD. The conditions are that the second derivative of cost function E with respect to both HMT and weight W must be positive. BPGAD was tested by simulation, which shows that BPGAD is much better in converging speed, learning ability and stability in comparison with BP, and can be applied to engineering practices.
  • Keywords
    backpropagation; gradient methods; neural nets; back propagation neural network; converging speed; gradient ascend algorithm; gradient descend algorithm; learning ability; learning efficiency; neural network algorithm; Algorithm design and analysis; Automation; Cost function; Electronic mail; Information analysis; Intelligent control; Neural networks; Stability; Testing; efficiency analysis; gradient ascend and descend; neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4244-2113-8
  • Electronic_ISBN
    978-1-4244-2114-5
  • Type

    conf

  • DOI
    10.1109/WCICA.2008.4593995
  • Filename
    4593995
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2495256