Sine Chaotification Model for Enhancing Chaos and Its Hardware Implementation

When chaotic systems are used in different practical applications, such as nonlinear control and cryptography, their complex chaos dynamics are strongly required. However, many existing chaotic systems have simple complexity, and this brings negative effects to chaos-based applications. To address t...

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Veröffentlicht in:IEEE transactions on industrial electronics (1982) Jg. 66; H. 2; S. 1273 - 1284
Hauptverfasser: Hua, Zhongyun, Zhou, Binghang, Zhou, Yicong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Chaos
Chaos theory
chaos-based application
Chaotic system
chaotification
Complexity
Complexity theory
Cryptography
Field programmable gate arrays
field-programmable gate array (FPGA) implementation
Nonlinear control
Nonlinear control systems
Pseudorandom
Random number generation
Trigonometric functions
ISSN:0278-0046, 1557-9948
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Zusammenfassung:When chaotic systems are used in different practical applications, such as nonlinear control and cryptography, their complex chaos dynamics are strongly required. However, many existing chaotic systems have simple complexity, and this brings negative effects to chaos-based applications. To address this issue, this paper introduces a sine chaotification model (SCM) as a general framework to enhance the chaos complexity of existing one-dimensional (1-D) chaotic maps. The SCM uses a sine function as a nonlinear chaotification transform and applies it to the output of a 1-D chaotic map. The resulting enhanced chaotic map of the SCM has better chaos complexity and a much larger chaotic range than the seed map. Theoretical analysis verifies the efficiency of the SCM. To show the performance of the SCM, we apply SCM to three existing chaotic maps and analyze the dynamics properties of the obtained enhanced chaotic maps. Performance evaluations prove that the three enhanced chaotic maps have more complicated dynamics behaviors than their seed chaotic maps. To show the implementation simplicity of the SCM, we implement the three enhanced chaotic maps using the field-programmable gate array. To investigate the SCM in practical application, we design pseudorandom number generators using the enhanced chaotic maps.
Bibliographie:ObjectType-Article-1
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2018.2833049