• DocumentCode
    3087134
  • Title

    Research and application on improved BP neural network algorithm

  • Author

    Xie, Rong ; Wang, Xinmin ; Li, Yan ; Zhao, Kairui

  • Author_Institution
    Sch. of Autom., Northwestern Polytech. Univ., Xi´´an, China
  • fYear
    2010
  • fDate
    15-17 June 2010
  • Firstpage
    1462
  • Lastpage
    1466
  • Abstract
    As the iterations are much, and the adjustment speed is slow, the improvements are made to the standard BP neural network algorithm. The momentum term of the weight adjustment rule is improved, make the weight adjustment speed more quicker and the weight adjustment process more smoother. The simulation of a concrete example shows that the iterations of the improved BP neural network algorithm can be calculated and compared. Finally, choosing a certain type of airplane as the controlled object, the improved BP neural network algorithm is used to design the control law for control command tracking, the simulation results show that the improved BP neural network algorithm can realize quicker convergence rate and better tracking accuracy.
  • Keywords
    backpropagation; convergence; iterative methods; neural nets; airplane; control command tracking; control law; improved BP neural network algorithm; iterations; Artificial neural networks; Automation; Biological neural networks; Convergence; Feedforward neural networks; Feedforward systems; Flowcharts; Neural networks; Neurons; Standards development; convergence rate; improved BP neural networ; learning rate; momentum term; weight adjustment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on
  • Conference_Location
    Taichung
  • Print_ISBN
    978-1-4244-5045-9
  • Electronic_ISBN
    978-1-4244-5046-6
  • Type

    conf

  • DOI
    10.1109/ICIEA.2010.5514820
  • Filename
    5514820