DocumentCode
3485263
Title
Elevator group control system tuned by a fuzzy neural network applied method
Author
Imasaki, Naoki ; Kubo, Susumu ; Nakai, Shoji ; Yoshitsugu, Tatsuo ; Kiji, Jun-Ichi ; Endo, Tsunekazu
Author_Institution
Syst. & Software Eng. Lab., Toshiba Corp., Kawasaki, Japan
Volume
4
fYear
1995
fDate
20-24 Mar 1995
Firstpage
1735
Abstract
We have developed a high-performance elevator group control system with a performance tuning function, which employs a fuzzy neural network as a performance forecasting model of the elevator system. The fuzzy neural network, which is a structured neural network based on a fuzzy reasoning framework, stores the correlation between control-parameters and the response of the elevator group as a fuzzy rule set. It performs a fuzzy rule-based reasoning to forecast the system performance of the elevator group. The performance tuning function utilizes the forecasting model in order to search the optimal control parameters which give the best system performance in the present traffic situation. The fuzzy neural network applied system can automatically adapt itself to various traffic situations. This paper gives the overview of the elevator group control system with the fuzzy neural network and shows the validity of the methodology by some simulation results
Keywords
adaptive control; fuzzy control; fuzzy neural nets; inference mechanisms; intelligent control; knowledge based systems; lifts; optimal control; adaptive control; elevator group control system; fuzzy neural network; fuzzy rule set; fuzzy rule-based reasoning; optimal control; performance forecasting model; performance tuning function; Communication system traffic control; Control system synthesis; Control systems; Elevators; Fuzzy control; Fuzzy neural networks; Fuzzy reasoning; Predictive models; System performance; Traffic control;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE Int
Conference_Location
Yokohama
Print_ISBN
0-7803-2461-7
Type
conf
DOI
10.1109/FUZZY.1995.409916
Filename
409916
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