NDLI: Chaos characteristics of peak elevator traffic flow

Content Provider	IEEE Xplore Digital Library
Author	Li Jun-Fang Leng Jian-Wei Zhang Jing-Long
Copyright Year	2011
Description	Author affiliation: School of Electrical Engineering and Automation, Tianjin University, 300072, China (Li Jun-Fang; Zhang Jing-Long) \|\| School of Electrical Engineering, Tianjin University of Technology, 300384, China (Leng Jian-Wei)
Abstract	In this paper, nonlinear time series techniques are applied to analyze the peak elevator traffic flow data. The phase space, which describes the evolution of the behaviour of a nonlinear system, is reconstructed using the delay embedding theorem suggested by Takens. The embedding parameters, e.g. the delay time and the embedding dimension are estimated using the mutual information of the data and the false nearest neighbor algorithm, respectively. Numerically the attractor of the elevator traffic flow from reconstruction is not necessarily sufficient indication of chaos, therefore we then calculate the correlation dimension of the resulting attractor and the largest Lyapunov exponent. It is demonstrated that the traffic flows of the up-peak and down-peak all exhibit low-dimensional chaotic behaviour. The result will help to adjust the group control scheduling methods according to the chaotic behaviour of the peak flow so as to increase the performance index.
Starting Page	5552
Ending Page	5555
File Size	98137
Page Count	4
File Format	PDF
ISBN	9781457706776
ISSN	21612927
e-ISBN	9789881725592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Chaos Wavelet transforms Largest Lyapunov Exponent Phase Space Reconstruction Correlation Time series analysis Elevators Elevator Traffic Flow Delay Mutual information
Content Type	Text
Resource Type	Article

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