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中文核心期刊

复杂时间序列的多尺度分析

MULTISCALE ANALYSIS OF COMPLEX TIME SERIES

  • 摘要: 为纪念郑哲敏先生仙逝一周年, 作者回忆在读研期间先生对自己的教诲, 介绍先生和自己在非线性科学领域的一些工作, 及后来这些工作如何被拓展并演变成一些非线性问题通用的分析方法. 具体的方法包括混沌时间序列分析的相空间最优重构, 混沌的直接动力学判据, 基于依赖于尺度的李雅普诺夫指数(SDLE)的多尺度分析及自适应分形分析. 特别, SDLE可以非常好地刻画已知的所有类的时间序列模型, 因此, 可以统一已知的各种复杂性的度量. 自适应分形分析基于自适应滤波, 能非常好地决定趋势, 包括各种震荡模式和回归分析的非线性回归曲线, 也能非常好地去噪, 并把时间序列分解成各种内在固有模式. 这些方法已被广泛用于自然科学、工程技术和社会科学的诸多领域. 它们特别适用于各领域(包括运维)的故障诊断, 生物医学数据的分析及不确定性的度量. 郑先生从不囿于他自己熟悉的领域, 而是随着时代发展不断拓展新的领域. 身处百年未遇之大变局时期, 我们必须发扬光大先生的这种求真务实和开拓进取的精神.

     

    Abstract: To commemorate the first anniversary of Prof. Zheng Zhemin's death, the author first recalls the teachings that Prof. Zheng gave to himself during his postgraduate studies, then introduces their joint work in nonlinear science, which have been expanded and evolved into some general methods for analyzing nonlinear problems. Specific methods include optimal embedding of chaotic time series, direct dynamical test for chaos, multiscale analysis based on scale-dependent Lyapunov exponent (SDLE), and adaptive fractal analysis. In particular, SDLE can very well characterize all known time series models, and therefore, can unify various measures of complexity developed thus far. Adaptive fractal analysis is based on adaptive filtering, which can optimally determine trends, including various oscillation modes and nonlinear regression curves in regression analysis, and can also optimally reduce noise and decompose time series into intrinsic modes. These methods have been widely used in many fields of natural sciences, engineering, and social sciences. They are particularly useful for fault diagnosis in various fields (including operation and maintenance), analysis of biomedical data, and measurement of uncertainty. Prof. Zheng was never confined to a field of comfort, but constantly expanded into new the ones with time going by. In a period with unprecedented changes not seen in a century, we must carry forward the spirit of Prof. Zheng.

     

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