Correlation Fuzzy measure of multivariate time series for signature recognition
Distinguishing different time series, which is determinant or stochastic, is an important task in signal processing. In this work, a correlation measure constructs Correlation Fuzzy Entropy (CFE) to discriminate Chaos and stochastic series. It can be employed to distinguish chaotic signals from ARIMA series with different noises. With specific embedding dimensions, we implemented the CFE features by analyzing two available online signature databases MCYT-100 and SVC2004. The accurate rates of the CFE-based models exceed 99.3%.
Authors
- Difang Chen (ORCID: https://orcid.org/0000-0003-0211-619X)
- Xiao Zhou (ORCID: https://orcid.org/0000-0002-3105-8390)
- Jinyu Xu (ORCID: https://orcid.org/0000-0001-8293-3653)
- Qingqing Wan
- Wenming Cheng (ORCID: https://orcid.org/0000-0002-9574-9481)
- Zelin Zhang (ORCID: https://orcid.org/0000-0002-5593-7859)
- Jun Wu
Institutions
- Hubei University of Automotive Technology (CN)
- Applied Mathematics (United States) (US)
- Hubei University (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1371/journal.pone.0309262
- Primary Topic
- Chaos control and synchronization
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Hubei Provincial Department of Education
- Natural Science Foundation of Hubei Province
- Hubei University of Automotive Technology
- Hubei University