Physica2022,Vol.58811.DOI:10.1016/j.physa.2021.126563

Recognition of the scale-free interval for calculating the correlation dimension using machine learning from chaotic time series

Zhou, Shuang Wang, Xingyuan Zhang, Chuan Zhou, Wenjie
Physica2022,Vol.58811.DOI:10.1016/j.physa.2021.126563

Recognition of the scale-free interval for calculating the correlation dimension using machine learning from chaotic time series

Zhou, Shuang 1Wang, Xingyuan 2Zhang, Chuan 3Zhou, Wenjie4
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作者信息

  • 1. Chongqing Normal Univ
  • 2. Dalian Maritime Univ
  • 3. Qufu Normal Univ
  • 4. Lanzhou Jiaotong Univ
  • 折叠

Abstract

Identifying the scale-free interval is an important step in calculating the correlation dimension. In this paper, we propose a method using machine learning known as density peak based clustering algorithm to recognize the scale-free interval. First, the GP algorithm is used for computing the correlation integral index. Then, the density peak based clustering algorithm is used for classifying the second-order derivative data sets of the correlation integral curve, the zero-fluctuation data are selected to be retained, and then the gross errors are excluded from the selected data. Finally, the coefficient of determination is used to identify the scale-free interval. Some examples are provided to verify the proposed method effective. The calculated results show that our method is feasible. In addition, this research proposes a new method to identify the scale-free interval for fractional dimension calculation theory. (C) 2021 Elsevier B.V. All rights reserved.

Key words

Scale-free interval/Correlation dimension/Fractal dimension/Chaotic time series/ALGORITHM/REGIME/MATRIX/DELAY/BOX

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出版年

2022
Physica

Physica

ISSN:0378-4371
被引量9
参考文献量66
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