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The instantaneous Lyapunov exponent and its application to chaotic dynamical systems

机译:瞬时Lyapunov指数及其在混沌动力学系统中的应用。

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摘要

Any system containing at least one positive Lyapunov exponent is defined to be chaotic and the system dynamics become unpredictable. For a mechanical system, the sum of Lyapunov exponents is negative and related to the damping, and so can be utilised to monitor any changes of the damping mechanism. However, in order to track any changes the data segment used to compute the Lyapunov exponents must be short. This leads to problems since Lyapunov exponents are calculated from a long term averaged divergence rate. To overcome this problem, it is described how the sum of Instantaneous Lyapunov exponents is related to the generalised divergence of the flow and the damping of a mechanical system. Computer simulations using differential equations are implemented to demonstrate the significance of the sum of Instantaneous Lyapunov exponents. In practice, it may be difficult to obtain accurate Instantaneous Lyapunov exponents from a time series (from experimental data), and so short term averaged Lyapunov exponents are introduced to overcome this. The sum of short term averaged Lyapunov exponents is applied to experimental data to detect evolutionary changes in damping from high to low. (C) 1998 Academic Press. [References: 17]
机译:任何包含至少一个正Lyapunov指数的系统都被定义为混乱的,并且系统动力学变得不可预测。对于机械系统,李雅普诺夫指数的总和为负,并且与阻尼有关,因此可以用来监视阻尼机制的任何变化。但是,为了跟踪任何更改,用于计算Lyapunov指数的数据段必须较短。由于Lyapunov指数是根据长期平均发散率计算得出的,因此会出现问题。为了克服这个问题,描述了瞬时李雅普诺夫指数的总和与流动的广义发散和机械系统的阻尼之间的关系。使用微分方程进行计算机仿真,以证明瞬时Lyapunov指数和的重要性。在实践中,从时间序列(从实验数据)中获取准确的瞬时Lyapunov指数可能很困难,因此引入了短期平均Lyapunov指数来克服这一问题。将短期平均Lyapunov指数的总和应用于实验数据,以检测阻尼从高到低的演化变化。 (C)1998年学术出版社。 [参考:17]

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