Tipping Point Detection Using Reservoir Computing  

作　　者：Xin Li Qunxi Zhu Chengli Zhao Xuzhe Qian Xue Zhang Xiaojun Duan Wei Lin 

机构地区：[1]College of Science,National University of Defense Technology,Changsha,Hunan 410073,China [2]Research Institute of Intelligent Complex Systems and MOE Frontiers Center for Brain Science,Fudan University,Shanghai 200433,China [3]Shanghai Artificial Intelligence Laboratory,Shanghai 200232,China [4]School of Mathematical Sciences,SCMS,SCAM,and CCSB,Fudan University,Shanghai 200433,China

出　　处：《Research》2024年第1期779-790,共12页研究（英文）

基　　金：the China Postdoctoral Science Foundation(no.2022M720817);by the Shanghai Postdoctoral Excellence Program(no.2021091);by the STCSM(nos.21511100200,22ZR1407300,and 23YF1402500);W.L.is supported by the National Natural Science Foundation of China(no.11925103);by the STCSM(nos.22JC1402500,22JC1401402,and 2021SHZDZX0103).

摘　　要：Detection in high fidelity of tipping points,the emergence of which is often induced by invisible changes in internal structures or/and external interferences,is paramountly beneficial to understanding and predicting complex dynamical systems(CDSs).Detection approaches,which have been fruitfully developed from several perspectives(e.g.,statistics,dynamics,and machine learning),have their own advantages but still encounter difficulties in the face of high-dimensional,fluctuating datasets.Here,using the reservoir computing(RC),a recently notable,resource-conserving machine learning method for reconstructing and predicting CDSs,we articulate a model-free framework to accomplish the detection only using the time series observationally recorded from the underlying unknown CDSs.

关 键 词：COMPUTING RECORD PING 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]