By Topic

Singularity detection method of chaotic time series using wavelet multi-resolution analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jian Feng ; Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China ; Liang Dong ; Jinhai Liu

In this paper, wavelet multi-resolution analysis (WMRA) is applied to detect singularity in chaotic time series. Based on the analysis of the relationship among wavelet multi-resolution, Lipschitz exponent and signal singularity, we select Daubechies wavelet to decompose the chaotic signal in different scales. After reconstructing those signals decomposed, some of which contain singular information, the position of singularity in signals can be exactly found out. Furthermore, because of the case that the existence of noise in real chaotic system, we test the anti-interference of WMRA with white noise. The research conclusions show that WMRA not only has a strong ability for detecting singularity of chaotic time series signal, but also has a good effect on anti-interference.

Published in:

Industrial and Information Systems (IIS), 2010 2nd International Conference on  (Volume:1 )

Date of Conference:

10-11 July 2010