一种通过互补集合经验模态分解（CCEMD）和排列熵（PE）提高数字混沌序列复杂度的新算法。

A Novel Algorithm to Improve Digital Chaotic Sequence Complexity through CCEMD and PE.

作者信息

Fan Chunlei, Xie Zhigang, Ding Qun

机构信息

Electrical Engineering College, Heilongjiang University, Harbin 150080, China.

Department of Electronic and Information Engineering, Polytechnic University, Hong Kong 999077, China.

出版信息

Entropy (Basel). 2018 Apr 18;20(4):295. doi: 10.3390/e20040295.

DOI:10.3390/e20040295

PMID:33265386

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7512813/

Abstract

In this paper, a three-dimensional chaotic system with a hidden attractor is introduced. The complex dynamic behaviors of the system are analyzed with a Poincaré cross section, and the equilibria and initial value sensitivity are analyzed by the method of numerical simulation. Further, we designed a new algorithm based on complementary ensemble empirical mode decomposition (CEEMD) and permutation entropy (PE) that can effectively enhance digital chaotic sequence complexity. In addition, an image encryption experiment was performed with post-processing of the chaotic binary sequences by the new algorithm. The experimental results show good performance of the chaotic binary sequence.

摘要

本文介绍了一个具有隐藏吸引子的三维混沌系统。利用庞加莱截面分析了系统的复杂动力学行为，并通过数值模拟方法分析了平衡点和初值敏感性。此外，我们设计了一种基于互补总体经验模态分解（CEEMD）和排列熵（PE）的新算法，该算法能够有效提高数字混沌序列的复杂度。另外，利用新算法对混沌二进制序列进行后处理，开展了图像加密实验。实验结果表明混沌二进制序列具有良好的性能。