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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:TANG Hai-yan,QI Wei-gui,Dindyu.Prediction of elevator traffic flow based on SVM and phase space reconstruction[J].Journal of Harbin Institute Of Technology(New Series),2011,18(3):111-114.DOI:10.11916/j.issn.1005-9113.2011.03.021.
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Prediction of elevator traffic flow based on SVM and phase space reconstruction
Author NameAffiliation
TANG Hai-yan School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China 
QI Wei-gui School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China 
Dindyu School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China 
Abstract:
To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ETF.Firstly,the phase space reconstruction for elevator traffic flow time series (ETFTS) is processed.Secondly,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of ETF.Then prediction model of ETFTS based on SVM is founded.Finally,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some building.Meanwhile,it is compared with RBF neural network model.Simulation results show that the trend of factual traffic flow is better followed by predictive traffic flow.SVM algorithm has much better prediction performance.The fitting and prediction of ETF with better effect are realized.
Key words:  support vector machine  phase space reconstruction  prediction of elevator traffic flow  RBF neural network
DOI：10.11916/j.issn.1005-9113.2011.03.021
Clc Number:TP273
Fund:

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