• DocumentCode
    2483276
  • Title

    Identification research on improved PID neural network and its application

  • Author

    Shen, Yongjun ; Gu, Xingsheng ; Bao, Qiong

  • Author_Institution
    Res. Inst. of Autom., East China Univ. of Sci. & Technol., Shanghai
  • fYear
    2008
  • fDate
    25-27 June 2008
  • Firstpage
    2542
  • Lastpage
    2547
  • Abstract
    PID neural network (PID-NN) is a new type of dynamic feed-forward network which combines neural network with PID control strategy. It performs a perfect function in process control with the merit of both general PID controller and neural network. In this paper, the concepts of variable integral and partial differential are introduced in the design of hidden-layer of PID-NN to improve the capabilities of neurons. The structure of system identification is analyzed, and the results of simulation with field data of wet FGD indicate the validity and superiority of this improved modeling approach.
  • Keywords
    feedforward; neurocontrollers; partial differential equations; three-term control; PID control strategy; PID neural network; dynamic feed-forward network; partial differential; variable integral; Automatic control; Automation; Electronic mail; Feedforward neural networks; Feedforward systems; Intelligent control; Neural networks; Neurons; Process control; Three-term control; ID neural network; identification; partial differential; variable integral;
  • 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.2008.4593323
  • Filename
    4593323