• DocumentCode
    3153566
  • Title

    Modern elevator group supervisory control systems and neural networks technique

  • Author

    Dewen, Zhu ; Li, Jiang ; Yuwen, Zhou ; Guanghui, Shan ; Kai, He

  • Author_Institution
    Dept. of Autom. Control, Shenyang Inst. of Archit. & Civil Eng., China
  • Volume
    1
  • fYear
    1997
  • fDate
    28-31 Oct 1997
  • Firstpage
    528
  • Abstract
    In this paper, the application of neural networks to modern elevator group supervisory control systems is discussed. The significance of introducing neural networks is presented. Artificial neural networks and fuzzy neural networks are described for turning the status of elevator running speciality, elevator equipment and building speciality into training data of elevator group supervisory control systems. Practical application shows that the elevator group supervisory control systems with neural networks can avoid the bunching phenomena effectively. Compared with the elevator group supervisory control systems with fuzzy rules, the elevator group supervisory control systems with neural networks are more superior
  • Keywords
    fuzzy control; fuzzy neural nets; learning (artificial intelligence); lifts; neurocontrollers; position control; bunching phenomena; elevator group supervisory control systems; fuzzy control; fuzzy neural networks; fuzzy rules; neural networks; neurocontrol; training data; Artificial neural networks; Communication system traffic control; Control systems; Elevators; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Neural networks; Supervisory control; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    0-7803-4253-4
  • Type

    conf

  • DOI
    10.1109/ICIPS.1997.672839
  • Filename
    672839