Synchronization of machine learning oscillators in complex networks

被引:9
作者
Weng, Tongfeng [1 ,2 ]
Chen, Xiaolu [3 ]
Ren, Zhuoming [1 ,2 ]
Yang, Huijie [3 ]
Zhang, Jie [4 ]
Small, Michael [5 ,6 ]
机构
[1] Hangzhou Normal Univ, Inst Informat Econ, Hangzhou 311121, Peoples R China
[2] Hangzhou Normal Univ, Alibaba Business Coll, Hangzhou 311121, Peoples R China
[3] Univ Shanghai Sci & Technol, Business Sch, Shanghai 200093, Peoples R China
[4] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China
[5] Univ Western Australia, Dept Math & Stat, Complex Syst Grp, Crawley, WA 6009, Australia
[6] CSIRO, Mineral Resources, Kensington, WA 6151, Australia
基金
中国国家自然科学基金;
关键词
Reservoir computing approach; Complex networks; Synchronization; Bifurcation; CHAOTIC SYSTEMS; POPULATIONS;
D O I
10.1016/j.ins.2023.02.028
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We study synchronization phenomena in complex networks in terms of machine learning oscillators without conventional dynamical equations. Specifically, we adopt an effective machine learning technique known as reservoir computing for modeling dynamical systems of interest. By constructing a coupled configuration, we show that a collection of coupled reservoir oscillators are in identical synchrony over a wider window of coupling strengths. We find that the geometrical and dynamical properties of synchronous orbits are in excellent agreement with that of the learned dynamical system. Remarkably, through this synchronization scheme, we successfully recover an almost identical bifurcation behavior of an observed system via merely its chaotic dynamics. Our work provides an alternative framework for studying synchronization phenomena in nature when only observed data are available.
引用
收藏
页码:74 / 81
页数:8
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