4.6 Article

Analysis of Chaotic Dynamics by the Extended Entropic Chaos Degree

期刊

ENTROPY
卷 24, 期 6, 页码 -

出版社

MDPI
DOI: 10.3390/e24060827

关键词

chaos; Lyapunov exponent; extended entropic chaos degree

资金

  1. JSPS KAKENHI [21K12063]
  2. Grants-in-Aid for Scientific Research [21K12063] Funding Source: KAKEN

向作者/读者索取更多资源

The Lyapunov exponent is a commonly used measure for quantifying chaos in a dynamical system, but its computation requires specific information. The entropic chaos degree quantifies chaos in a dynamical system as an information quantity and can be computed directly for any time series, regardless of knowledge about the dynamical system. A recent study introduced the extended entropic chaos degree, which achieved the same value as the sum of Lyapunov exponents under typical chaotic conditions. An improved computation formula for the extended entropic chaos degree was proposed to obtain accurate numerical results for multidimensional chaotic maps. This study demonstrates that all Lyapunov exponents of a chaotic map can be estimated to compute the extended entropic chaos degree and proposes a computational algorithm for it, which was applied to one and two-dimensional chaotic maps. The results suggest that the extended entropic chaos degree may serve as a viable alternative to the Lyapunov exponent for both one and two-dimensional chaotic dynamics.
The Lyapunov exponent is the most-well-known measure for quantifying chaos in a dynamical system. However, its computation for any time series without information regarding a dynamical system is challenging because the Jacobian matrix of the map generating the dynamical system is required. The entropic chaos degree measures the chaos of a dynamical system as an information quantity in the framework of Information Dynamics and can be directly computed for any time series even if the dynamical system is unknown. A recent study introduced the extended entropic chaos degree, which attained the same value as the total sum of the Lyapunov exponents under typical chaotic conditions. Moreover, an improved calculation formula for the extended entropic chaos degree was recently proposed to obtain appropriate numerical computation results for multidimensional chaotic maps. This study shows that all Lyapunov exponents of a chaotic map can be estimated to calculate the extended entropic chaos degree and proposes a computational algorithm for the extended entropic chaos degree; furthermore, this computational algorithm was applied to one and two-dimensional chaotic maps. The results indicate that the extended entropic chaos degree may be a viable alternative to the Lyapunov exponent for both one and two-dimensional chaotic dynamics.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Mathematics, Applied

An extension of the entropic chaos degree and its positive effect

Kei Inoue, Tomoyuki Mao, Hidetoshi Okutomi, Ken Umeno

Summary: The Lyapunov exponent is used to quantify the chaos of a dynamical system, while entropic chaos degree is introduced in information dynamics to measure the strength of chaos. Both can be used to compute the chaos degree of a dynamical system. This paper attempts to extend the concept of entropic chaos degree in Euclidean space to enhance the measurement capability of chaos strength in dynamical systems, showing several relations with the Lyapunov exponent.

JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS (2021)

Article Physics, Multidisciplinary

An Improved Calculation Formula of the Extended Entropic Chaos Degree and Its Application to Two-Dimensional Chaotic Maps

Kei Inoue

Summary: The paper discusses the application of Lyapunov exponent and entropic chaos degree in quantifying chaos in dynamical systems, with a focus on reducing the difference between the two measures. It also presents an extension of the improved entropic chaos degree for multi-dimensional chaotic maps and proposes an improved calculation formula for obtaining suitable numerical computation results for two-dimensional chaotic maps.

ENTROPY (2021)

暂无数据