李雅普诺夫指数
混沌理论
关联维数
混乱的
系列(地层学)
噪音(视频)
混沌(操作系统)
非线性系统
滤波器(信号处理)
维数(图论)
计算机科学
统计物理学
嵌入
数学
控制理论(社会学)
物理
分形
分形维数
数学分析
人工智能
古生物学
计算机安全
控制(管理)
量子力学
纯数学
图像(数学)
计算机视觉
生物
作者
Christopher Frazier,Kara M. Kockelman
摘要
Chaos theory is used to analyze highly complex systems and thus may be useful for transportation applications. A series of analyses with which to find and exploit chaos is outlined, including time delays and embedding dimensions, Fourier power series, the correlation dimension, the largest Lyapunov exponent, and predictions. As an example, traffic flow data are analyzed and found to be chaotic, although it is shown that this could be the result of high-frequency noise. When used with a low-pass filter, predictions based on chaos theory are shown to have greater predictive power than a nonlinear least-squares method.
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