Duffing振子中的有序模式：特征化能力分析
Ordinal Patterns in the Duffing Oscillator: Analyzing Powers of Characterization
摘要：
有序模式是一种时间序列数据分析工具，用作构建置换熵的初步步骤，置换熵本身允许将动力学表征为混沌或规则，如李雅普诺夫指数等更多理论结构。然而，有序模式存储严格比置换熵或李雅普诺夫指数更多的信息。我们目前的结果与Duffing振子显示，有序模式反映了其他措施，甚至排列熵看不见的动态对称性的变化。我们发现，在给定的参数值的对称性的这些变化与稳定性的变化在相邻的参数，这表明了一种新的预测能力，这种分析技术。
英文摘要：
Ordinal Patterns are a time-series data analysis tool used as a preliminary step to construct the Permutation Entropy which itself allows the same characterization of dynamics as chaotic or regular as more theoretical constructs such as the Lyapunov exponent. However ordinal patterns store strictly more information than Permutation Entropy or Lyapunov exponents. We present results working with the Duffing oscillator showing that ordinal patterns reflect changes in dynamical symmetry invisible to other measures, even Permutation Entropy. We find that these changes in symmetry at given parameter values are correlated with a change in stability at neighboring parameters which suggests a novel predictive capability for this analysis technique.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
