Applications of Nonlinear Methods to Signal Detection of Time Series

The analysis of time series from real system is the most direct link between nonlinear theory and real world. If the measure data from nonlinear system are described linearly, useful signal could not found out. The nonlinear methods in this paper, Poincaré map, fractal dimension, and correlation di...

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Bibliographic Details
Published in2013 Fifth International Conference on Measuring Technology and Mechatronics Automation pp. 306 - 309
Main Authors Liming Lin, Yingxiang Wu, Xingfu Zhong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2013
Subjects
Chaos
Correlation
Fractals
Manifolds
Nonlinear Methods
Oscillators
Signal Analysis
Signal detection
Time Series
Time series analysis
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ISBN9781467356527
1467356522
ISSN2157-1473
DOI10.1109/ICMTMA.2013.79

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Summary:The analysis of time series from real system is the most direct link between nonlinear theory and real world. If the measure data from nonlinear system are described linearly, useful signal could not found out. The nonlinear methods in this paper, Poincaré map, fractal dimension, and correlation dimension, are introduced to detect chaos phenomena in a system. These nonlinear algorithms can be used to pick up signal characteristics of time series. Some examples are presented to illustrate how to apply these methods in signal detection and engineering signal analysis.
ISBN:9781467356527
1467356522
ISSN:2157-1473
DOI:10.1109/ICMTMA.2013.79