DocumentCode :
2664895
Title :
A multi-mode prediction method for elevator traffic flow based on classification off-line
Author :
Qun, Zong ; Weijia, Wang ; Anna, Shang
Author_Institution :
Sch. of Electr. Eng. & Autom., Tianjin Univ., Tianjin
fYear :
2008
fDate :
16-18 July 2008
Firstpage :
522
Lastpage :
526
Abstract :
In order to achieve the on-line traffic flow prediction, a novel multi-mode prediction method based on mode classification off-line is proposed. Firstly, the elevator traffic flow is classified off-line into patterns by the two-stage Clustering Algorithm, Artificial Immune C-Means Clustering Algorithm (AI C-Means CA). Then Gaussian Mixture Model (GMM) is used to model the multi-mode elevator traffic flow. And the EM algorithm is utilized to estimate the parameters of GMM to predict elevator traffic flow on line. Finally, the effectivity of this prediction method is validated by comparing simulation results with other prediction methods.
Keywords :
Gaussian processes; artificial immune systems; expectation-maximisation algorithm; lifts; pattern clustering; Gaussian mixture model; artificial immune c-means clustering algorithm; elevator traffic flow; expectation-maximisation algorithm; multimode prediction; offline mode classification; online traffic flow prediction; Artificial intelligence; Clustering algorithms; Data mining; Dispatching; Elevators; Pattern analysis; Pattern recognition; Prediction methods; Predictive models; Traffic control; Artificial Immune C-Means Clustering Algorithm; EM Algorithm; Elevator Traffic Flow; Gaussian Mixture Model; Multi-mode Prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location :
Kunming
Print_ISBN :
978-7-900719-70-6
Electronic_ISBN :
978-7-900719-70-6
Type :
conf
DOI :
10.1109/CHICC.2008.4605447
Filename :
4605447
Link To Document :
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