NDLI: A new method on solving correlation dimension of chaotic time-series

Content Provider	IEEE Xplore Digital Library
Author	Qiao Meiying Ma Xiaoping
Copyright Year	2012
Description	Author affiliation: School of Information and Electrical Engineering, China University Mining and Technology, Xuzhou, Jiangsun Province, China (Ma Xiaoping) \|\| School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, China (Qiao Meiying)
Abstract	Traditional G-P algorithm exist two drawbacks in solving the correlation dimension of chaotic time series. The one is the subjective existence to determine scaleless range, the other is calculation error is large when the amount of data is small. For two shortcomings, the fuzzy C-means clustering is introduced to the G-P algorithm to determine the no-scales range. Least-squares fitting method is used to find the saturation correlation dimension value in determining the scalelesss range. Using different amount of Loren and Rossler data, such as 500, 1000, 2000, 5000 and 10000, verify the improved algorithm in this paper,. Simulation results show that the error relatively small if the delay time is small when the amount of 500, 1000 and 2000. With the length of data increases, the cluster centre value of the slope relatively flat closer to their ideal value. The conclusions are applicable to Lorenz and Rossler data.
Starting Page	4820
Ending Page	4824
File Size	631122
Page Count	5
File Format	PDF
ISBN	9781467313971
e-ISBN	9781467313988
DOI	10.1109/WCICA.2012.6359391
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Clustering algorithms Delay Algorithm design and analysis Equations Indexes Mathematical model Correlation dimension Chaotic tim-series G-P algorithm
Content Type	Text
Resource Type	Article

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