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
Link To Document :
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