Predicting Catastrophes in Nonlinear Dynamical Systems by Compressive Sensing

被引：237

作者：

Wang, Wen-Xu ^{[1
]}

Yang, Rui ^{[1
]}

Lai, Ying-Cheng ^{[1
,2
]}

Kovanis, Vassilios ^{[3
]}

Grebogi, Celso ^{[2
]}

机构：

[1] Arizona State Univ, Sch Elect Comp & Energy Engn, Tempe, AZ 85287 USA

[2] Univ Aberdeen, Kings Coll, Inst Complex Syst & Math Biol, Aberdeen AB24 3UE, Scotland

[3] Sensors Directorate, Wright Patterson AFB, OH 45433 USA

来源：

PHYSICAL REVIEW LETTERS | 2011年 / 106卷 / 15期

基金：

英国生物技术与生命科学研究理事会;

关键词：

CRISES; CHAOS;

D O I：

10.1103/PhysRevLett.106.154101

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

An extremely challenging problem of significant interest is to predict catastrophes in advance of their occurrences. We present a general approach to predicting catastrophes in nonlinear dynamical systems under the assumption that the system equations are completely unknown and only time series reflecting the evolution of the dynamical variables of the system are available. Our idea is to expand the vector field or map of the underlying system into a suitable function series and then to use the compressive-sensing technique to accurately estimate the various terms in the expansion. Examples using paradigmatic chaotic systems are provided to demonstrate our idea.

引用

页数：4

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