Complexity of chaotic binary sequence and precision of its numerical simulation

Liu, Niansheng, Guo, Donghui and Parr, Gerard ORCID: https://orcid.org/0000-0002-9365-9132 (2012) Complexity of chaotic binary sequence and precision of its numerical simulation. Nonlinear Dynamics, 67. pp. 549-556. ISSN 1573-269X

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Abstract

Numerical simulation is one of primary methods in which people study the property of chaotic systems. However, there is the effect of finite precision in all processors which can cause chaos to degenerate into a periodic function or a fixed point. If it is neglected the precision of a computer processor for the binary numerical calculations, the numerical simulation results may not be accurate due to the chaotic nature of the system under study. New and more accurate methods must be found. A quantitative computable method of sequence complexity evaluation is introduced in this paper. The effect of finite precision is evaluated from the viewpoint of sequence complexity. The simulation results show that the correlation function based on information entropy can effectively reflect the complexity of pseudorandom sequences generated by a chaotic system, and it is superior to the other measure methods based on entropy. The finite calculation precision of the processor has significant effect on the complexity of chaotic binary sequences generated by the Lorenz equation. The pseudorandom binary sequences with high complexity can be generated by a chaotic system as long as the suitable computational precision and quantification algorithm are selected and behave correctly. The new methodology helps to gain insight into systems that may exist in various application domains such as secure communications and spectrum management.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory Faculty of Science > Research Groups > Data Science and AI
Depositing User:	Pure Connector
Date Deposited:	24 Sep 2016 00:34
Last Modified:	10 Dec 2024 01:28
URI:	https://ueaeprints.uea.ac.uk/id/eprint/60101
DOI:	10.1007/s11071-011-0005-7

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