JOURNAL ARTICLE

Parameter-adaptive identical synchronization disclosing Lorenz chaotic masking

Alicia d’AnjouC. SarasolaFrancisco TorrealdeaRaúl Orduna-UrrutiaManuel Graña

Year: 2001 Journal:   Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics Vol: 63 (4)Pages: 046213-046213   Publisher: American Physical Society

Abstract

A parameter-adaptive rule that globally synchronizes oscillatory Lorenz chaotic systems with initially different parameter values is reported. In principle, the adaptive rule requires access to the three state variables of the drive system but it has been readapted to work with the exclusive knowledge of only one variable, a potential message carrier. The rule is very robust and can be used to trace parameter modulation conveying hidden messages. The driven system is defined according to a drive-driven type of coupling that guarantees synchronization if parameters are identical. From any arbitrary initial state, the parameters of the driven system are dynamically adapted to reach convergence to the drive parameter values. At this point, synchronization mismatch or parameter tracing is used to unmask any potential hidden message.

Keywords:
Chaotic Lorenz system Synchronization (alternating current) Tracing TRACE (psycholinguistics) Computer science State variable Control theory (sociology) Masking (illustration) Convergence (economics) Variable (mathematics) State (computer science) Synchronization of chaos Coupling (piping) Modulation (music) Point (geometry) Physics Mathematics Algorithm Mathematical analysis Artificial intelligence Channel (broadcasting) Telecommunications Control (management)

Metrics

32
Cited By
1.85
FWCI (Field Weighted Citation Impact)
27
Refs
0.85
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Chaos control and synchronization
Physical Sciences →  Physics and Astronomy →  Statistical and Nonlinear Physics
Nonlinear Dynamics and Pattern Formation
Physical Sciences →  Computer Science →  Computer Networks and Communications
Neural Networks Stability and Synchronization
Physical Sciences →  Computer Science →  Computer Networks and Communications

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