An efficient method of distinguishing chaos from noise

doi:10.1088/1674-1056/19/5/050505

中国物理B ›› 2010, Vol. 19 ›› Issue (5): 50505-050505.doi: 10.1088/1674-1056/19/5/050505

An efficient method of distinguishing chaos from noise

郭建秀¹, 魏恒东², 李立萍²

(1)College of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; (2)School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

收稿日期:2009-02-16 修回日期:2009-11-25 出版日期:2010-05-15 发布日期:2010-05-15

An efficient method of distinguishing chaos from noise

Wei Heng-Dong(魏恒东)^a), Li Li-Ping(李立萍)^a), and Guo Jian-Xiu(郭建秀)^b)

^a School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China; ^b College of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China

Received:2009-02-16 Revised:2009-11-25 Online:2010-05-15 Published:2010-05-15

摘要/Abstract

摘要： It is an important problem in chaos theory whether an observed irregular signal is deterministic chaotic or stochastic. We propose an efficient method for distinguishing deterministic chaotic from stochastic time series for short scalar time series. We first investigate, with the increase of the embedding dimension, the changing trend of the distance between two points which stay close in phase space. And then, we obtain the differences between Gaussian white noise and deterministic chaotic time series underlying this method. Finally, numerical experiments are presented to testify the validity and robustness of the method. Simulation results indicate that our method can distinguish deterministic chaotic from stochastic time series effectively even when the data are short and contaminated.

Abstract: It is an important problem in chaos theory whether an observed irregular signal is deterministic chaotic or stochastic. We propose an efficient method for distinguishing deterministic chaotic from stochastic time series for short scalar time series. We first investigate, with the increase of the embedding dimension, the changing trend of the distance between two points which stay close in phase space. And then, we obtain the differences between Gaussian white noise and deterministic chaotic time series underlying this method. Finally, numerical experiments are presented to testify the validity and robustness of the method. Simulation results indicate that our method can distinguish deterministic chaotic from stochastic time series effectively even when the data are short and contaminated.

Key words: phase space reconstruction, average false nearest neighbour, chaos detection

中图分类号: (Control of chaos, applications of chaos)

05.45.Gg

05.45.Xt (Synchronization; coupled oscillators) 02.30.Yy (Control theory)

魏恒东, 李立萍, 郭建秀. An efficient method of distinguishing chaos from noise[J]. 中国物理B, 2010, 19(5): 50505-050505.

Wei Heng-Dong(魏恒东), Li Li-Ping(李立萍), and Guo Jian-Xiu(郭建秀). An efficient method of distinguishing chaos from noise[J]. Chin. Phys. B, 2010, 19(5): 50505-050505.

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An efficient method of distinguishing chaos from noise

An efficient method of distinguishing chaos from noise

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