Characterizing dynamical transitions by statistical complexity measures based on ordinal pattern transition networks-Reference-Cited by-同舟云学术

Characterizing dynamical transitions by statistical complexity measures based on ordinal pattern transition networks

Published:2021-03 Issue:3 Volume:31 Page:033127
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Huang Min¹,Sun Zhongkui²^ORCID,Donner Reik V.³⁴^ORCID,Zhang Jie⁵,Guan Shuguang¹^ORCID,Zou Yong¹^ORCID

Affiliation:

1. School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China

2. Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an 710072, China

3. Department of Water, Environment, Construction and Safety, Magdeburg–Stendal University of Applied Sciences, Breitscheidstraße 2, 39114 Magdeburg, Germany

4. Potsdam Institute for Climate Impact Research (PIK) – Member of the Leibniz Association, Telegrafenberg A31, 14473 Potsdam, Germany

5. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China

Funder

National Natural Science Foundation of China

Shanghai Municipal Science and Technology Major Project and ZJ Lab

Natural Science Foundation of Shanghai

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0038876

Reference42 articles.

1. Complex network approaches to nonlinear time series analysis

2. Complex Network from Pseudoperiodic Time Series: Topology versus Dynamics

3. Complex network approach for recurrence analysis of time series

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