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Acta Physica Sinica
Article . 2011 . Peer-reviewed
Data sources: Crossref
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Acta Physica Sinica
Article
License: CC BY
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Complexity analysis of chaotic sequence based on the intensive statistical complexity algorithm

Authors: null Sun Ke-Hui; null He Shao-Bo; null Sheng Li-Yuan;

Complexity analysis of chaotic sequence based on the intensive statistical complexity algorithm

Abstract

To analyze the complexity of the chaotic sequences, based on the intensive statistical complexity algorithm, the complexities of the discrete TD-ERCS and continuous simplified Lorenz chaotic systems were investigated respectively, and the complexities of the chaotic sequences with different system parameters were calculated. The complexities of pseudo-random sequences of the continuous chaotic systems disordered by m-series and chaotic pseudo-random sequences were analyzed. The results indicate that the intensive statistical complexity algorithm is an effective method for analyzing the complexity of the chaotic sequences, and the complexity of the discrete chaotic systems is larger than that of the continuous ones. However, after disordering by m-series or chaotic pseudo-random sequences, the complexities of the pseudo-random sequences can be increased significantly. This study provides a theoretical basis for the applications of chaotic sequences in the field of secure communication and information encryption.

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    influence
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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Average
Top 10%
Top 10%
gold