• DocumentCode
    1924853
  • Title

    Simulation Study of PID Neural Network Temperature Control System in Plastic Injecting-Moulding Machine

  • Author

    Shu, Huai-lin ; Shu, Hua

  • Author_Institution
    Guangzhou Univ., Guangzhou
  • Volume
    1
  • fYear
    2007
  • fDate
    19-22 Aug. 2007
  • Firstpage
    492
  • Lastpage
    497
  • Abstract
    PID (proportional, integral and derivative) neural network is a special neural network in which the neurons have proportional, integral and derivative input-output functions and was first given by the author in 1997. The simulation about a three-stage heater in a plastic injection machine was introduced in this paper. The temperature control system of the plastic injection machine is a strong coupled multivariable system and the characteristics of the system are analyzed in the paper. The algorithms of PID neural network are given, the VB program of the back propagation algorithm was introduced and the simulation results are shown. The results prove that the PID neural network has perfect decoupling and self-learning control performances.
  • Keywords
    Visual BASIC; backpropagation; control engineering computing; injection moulding; learning systems; multivariable systems; neurocontrollers; plastics industry; self-adjusting systems; temperature control; three-term control; PID control; VB program; back propagation algorithm; coupled multivariable system; decoupling control; neural network; plastic injecting-moulding machine; self-learning control; temperature control system; Control systems; Cybernetics; MIMO; Machine learning; Neural networks; Neurons; Plastics; Temperature control; Temperature sensors; Three-term control; Decoupling control; Multivariable system; PID neural network; Temperature control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370195
  • Filename
    4370195