• DocumentCode
    2448416
  • Title

    Application of Recursive Predict Error Neural Networks in Mechanical Propertise Forecasting

  • Author

    Wang Wu ; Zhang Yuan-min

  • Author_Institution
    Electro-Inf. Coll., Xuchang Univ., Xuchang, China
  • fYear
    2009
  • fDate
    25-26 April 2009
  • Firstpage
    132
  • Lastpage
    135
  • Abstract
    Parameters control problem was crucial in rolling industrial, but the mechanical properties forecasting of strip steel was an information space incompletely and non-linear complex system which was hard for traditional method. Artificial neural networks was a non-linear system with strong non-linear modeling ability, but the traditional BP neural networks has many shortcomings like easily step into local minimum, with weak generalization ability and the middle layer neuron are hard to determine, so the artificial neural networks with recursive predict error (RPE) algorithm was proposed in this paper with the networkspsila structure, algorithm, sample data selection also presented, the simulation shows its effective and can successfully applied into parameters control of rolling industrial.
  • Keywords
    backpropagation; mechanical properties; neural nets; production engineering computing; rolling; sheet metal processing; BP neural network; artificial neural network; information space; mechanical properties forecasting; mechanical propertise forecasting; middle layer neuron; nonlinear complex system; nonlinear system; parameters control problem; recursive predict error neural network; rolling industrial; strip steel; strong nonlinear modeling; weak generalization ability; Aerospace industry; Artificial neural networks; Control systems; Electrical equipment industry; Industrial control; Mechanical factors; Metals industry; Neural networks; Nonlinear control systems; Predictive models; Neural networks; Recursive predict error(RPE) algorithm; mechanical propertise; simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence, 2009. JCAI '09. International Joint Conference on
  • Conference_Location
    Hainan Island
  • Print_ISBN
    978-0-7695-3615-6
  • Type

    conf

  • DOI
    10.1109/JCAI.2009.30
  • Filename
    5158957