基于动力系统序结构的熵确定
Entropy determination based on the ordinal structure of a dynamical system
摘要：
由于Bandt和Pompe的创新性工作，评估时间序列的顺序方法在非线性时间序列分析的其他技术中日益确立。本文总结和推广了由状态空间的序生成划分的递增序列确定保测度动力系统的Kolmogorov-Sinai熵的理论.我们的主要重点是在没有信息损失的情况下测量过程。特别地，我们考虑的问题的最小必要数量的测量有关的一个给定的动力系统的属性。
英文摘要：
The ordinal approach to evaluate time series due to innovative works of Bandt and Pompe has increasingly established itself among other techniques of nonlinear time series analysis. In this paper, we summarize and generalize the theory of determining the Kolmogorov-Sinai entropy of a measure-preserving dynamical system via increasing sequences of order generated partitions of the state space. Our main focus are measuring processes without information loss. Particularly, we consider the question of the minimal necessary number of measurements related to the properties of a given dynamical system.
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。
