• DocumentCode
    1573039
  • Title

    Modeling study of sludge process based on neural network

  • Author

    Yu, Ying ; Qiao, Junfei ; Ye, Xudong

  • Author_Institution
    Sch. of Electron. & Control Eng., Beijing Univ. of Technol., China
  • Volume
    4
  • fYear
    2004
  • Firstpage
    3413
  • Abstract
    On the basis of analyzing the classical methods of sludge process modeling, the paper put forward a new method about activated sludge process by neural networks. Firstly, the paper utilized principal component analysis method to realize reduce the dimension of the input vectors and orthogonalize the components of the input vectors. Then built activated sludge process system by BP and RBF artificial neural networks, the applicability of the two neural network models were analyzed to sludge process. The experiment result shows that: (1) these neural networks may reflect real conditions correctly and have strong self-adaptation; (2) the RBF neural network model has better convergence ability and impending speed than the BP neural network model.
  • Keywords
    backpropagation; principal component analysis; radial basis function networks; sludge treatment; RBF artificial neural networks; back propagation neural network; input vectors; principal component analysis; sludge process; Artificial neural networks; Control engineering; Convergence; Neural networks; Paper technology; Power supplies; Power system modeling; Principal component analysis; Sludge treatment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
  • Print_ISBN
    0-7803-8273-0
  • Type

    conf

  • DOI
    10.1109/WCICA.2004.1343176
  • Filename
    1343176