Complex network perspective on modelling chaotic systems via machine learning

doi:10.1088/1674-1056/abd9b3

中国物理B ›› 2021, Vol. 30 ›› Issue (6): 60506-060506.doi: 10.1088/1674-1056/abd9b3

Complex network perspective on modelling chaotic systems via machine learning

Tong-Feng Weng(翁同峰)^1,†, Xin-Xin Cao(曹欣欣)², and Hui-Jie Yang(杨会杰)³

1 Institute of Information Economy and Alibaba Business College, Hangzhou Normal University, Hangzhou 311121, China;
2 College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China;
3 Business School, University of Shanghai for Science and Technology, Shanghai 200093, China

收稿日期:2020-09-08 修回日期:2020-12-30 接受日期:2021-01-08 出版日期:2021-05-18 发布日期:2021-05-25
通讯作者: Tong-Feng Weng E-mail:wtongfeng2006@163.com
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 11805128), the Fund from Xihu Scholar award from Hangzhou City, and the Hangzhou Normal University Starting Fund (Grant No. 4135C50220204098).

Complex network perspective on modelling chaotic systems via machine learning

Tong-Feng Weng(翁同峰)^1,†, Xin-Xin Cao(曹欣欣)², and Hui-Jie Yang(杨会杰)³

1 Institute of Information Economy and Alibaba Business College, Hangzhou Normal University, Hangzhou 311121, China;
2 College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China;
3 Business School, University of Shanghai for Science and Technology, Shanghai 200093, China

Received:2020-09-08 Revised:2020-12-30 Accepted:2021-01-08 Online:2021-05-18 Published:2021-05-25
Contact: Tong-Feng Weng E-mail:wtongfeng2006@163.com
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 11805128), the Fund from Xihu Scholar award from Hangzhou City, and the Hangzhou Normal University Starting Fund (Grant No. 4135C50220204098).

摘要/Abstract

摘要： Recent advances have demonstrated that a machine learning technique known as "reservoir computing" is a significantly effective method for modelling chaotic systems. Going beyond short-term prediction, we show that long-term behaviors of an observed chaotic system are also preserved in the trained reservoir system by virtue of network measurements. Specifically, we find that a broad range of network statistics induced from the trained reservoir system is nearly identical with that of a learned chaotic system of interest. Moreover, we show that network measurements of the trained reservoir system are sensitive to distinct dynamics and can in turn detect the dynamical transitions in complex systems. Our findings further support that rather than dynamical equations, reservoir computing approach in fact provides an alternative way for modelling chaotic systems.

关键词: reservoir computing approach, complex networks, chaotic systems

Abstract: Recent advances have demonstrated that a machine learning technique known as "reservoir computing" is a significantly effective method for modelling chaotic systems. Going beyond short-term prediction, we show that long-term behaviors of an observed chaotic system are also preserved in the trained reservoir system by virtue of network measurements. Specifically, we find that a broad range of network statistics induced from the trained reservoir system is nearly identical with that of a learned chaotic system of interest. Moreover, we show that network measurements of the trained reservoir system are sensitive to distinct dynamics and can in turn detect the dynamical transitions in complex systems. Our findings further support that rather than dynamical equations, reservoir computing approach in fact provides an alternative way for modelling chaotic systems.

Key words: reservoir computing approach, complex networks, chaotic systems

中图分类号: (Nonlinear dynamics and chaos)

05.45.-a

89.75.Hc (Networks and genealogical trees)

Tong-Feng Weng(翁同峰), Xin-Xin Cao(曹欣欣), and Hui-Jie Yang(杨会杰). Complex network perspective on modelling chaotic systems via machine learning[J]. 中国物理B, 2021, 30(6): 60506-060506.

Tong-Feng Weng(翁同峰), Xin-Xin Cao(曹欣欣), and Hui-Jie Yang(杨会杰). Complex network perspective on modelling chaotic systems via machine learning[J]. Chin. Phys. B, 2021, 30(6): 60506-060506.

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Complex network perspective on modelling chaotic systems via machine learning

Complex network perspective on modelling chaotic systems via machine learning

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