Title :
Chaos characteristics of peak elevator traffic flow
Author :
Li Jun-fang ; Leng Jian-Wei ; Zhang Jing-long
Author_Institution :
Sch. of Electr. Eng. & Autom., Tianjin Univ., Tianjin, China
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.
Keywords :
chaos; delays; lifts; time series; wavelet transforms; Lyapunov exponent; chaos characteristics; continuous wavelet transformation; delay embedding theorem; delay time parameter; down-peak traffic flow; embedding dimension parameter; group control scheduling method; nonlinear time series technique; peak elevator traffic flow; phase space; up-peak traffic flow; Chaos; Correlation; Delay; Elevators; Mutual information; Time series analysis; Wavelet transforms; Chaos; Elevator Traffic Flow; Largest Lyapunov Exponent; Phase Space Reconstruction;
Conference_Titel :
Control Conference (CCC), 2011 30th Chinese
Conference_Location :
Yantai
Print_ISBN :
978-1-4577-0677-6
Electronic_ISBN :
1934-1768