Chaos Synchronization Between Unified Chaotic System And Rossler System

2013 ◽  
Vol 321-324 ◽  
pp. 2464-2470
Author(s):  
Hao Wu ◽  
Bo Lun Xu ◽  
Chang Fan ◽  
Xian Yong Wu
Keyword(s):  
Active Control ◽  
Chaotic System ◽  
Chaos Synchronization ◽  
Adaptive Synchronization ◽  
System Parameters ◽  
Unified Chaotic System ◽  
Rossler System ◽  
Response Systems ◽  
Rössler System ◽  
Different Chaotic Systems

In this paper, two synchronization schemes between two different chaotic systems are proposed. Chaos synchronization between unified chaotic system and Rossler system via active control and adaptive control are investigated. Different controllers are designed to synchronize the drive and response systems. Active control synchronization is used when system parameters are known; adaptive synchronization is employed when system parameters are unknown or uncertain. Simulation results show the effectiveness of the proposed schemes.

scholarly journals Chaos Synchronization between Fractional-Order Unified Chaotic System and Rossler Chaotic System

2012 ◽  
Vol 562-564 ◽  
pp. 2088-2091
Author(s):  
Xian Yong Wu ◽  
Yi Long Cheng ◽  
Kai Liu ◽  
Xin Liang Yu ◽  
Xian Qian Wu
Keyword(s):  
Fractional Order ◽  
Chaotic System ◽  
Chaos Synchronization ◽  
Chaotic Dynamics ◽  
Chaotic Attractors ◽  
System Parameters ◽  
Unified Chaotic System ◽  
Simulation Results ◽  
Drive Systems ◽  
Asymptotic Synchronization

The chaotic dynamics of the unified chaotic system and the Rossler system with different fractional-order are studied in this paper. The research shows that the chaotic attractors can be found in the two systems while the orders of the systems are less than three. Asymptotic synchronization of response and drive systems is realized by active control through designing proper controller when system parameters are known. Theoretical analysis and simulation results demonstrate the effective of this method.

Chaos Synchronization of Hyperchaos Rossler System

2014 ◽  
Vol 687-691 ◽  
pp. 724-727
Author(s):  
Lin Wu ◽  
Jin Cheng Wei ◽  
Liu Jie
Keyword(s):  
Numerical Simulation ◽  
Chaos Synchronization ◽  
Simulation Experiment ◽  
Nonlinear Feedback ◽  
Driving System ◽  
Lyapunov Stabilization ◽  
Rossler System ◽  
Rössler System

Taking the hyperchaos Rossler system as an example and based on the Lyapunov Stabilization Law, with the parameters unknown, through the designing of controller, the synchronization of the driving system and the responding system has been realized by using the nonlinear feedback controlling method. This method provides a convenient way for the synchrocontrol of hyperchaos system, whose effectiveness has been further proved by the numerical simulation experiment.

Evolutionary Algorithm Based Solution of Rössler Chaotic System Using Bernstein Polynomials

2020 ◽  
Vol 17 (7) ◽  
pp. 2932-2939
Author(s):  
Rania A. Alharbey ◽  
Kiran Sultan
Keyword(s):  
Chaotic System ◽  
Fitness Function ◽  
Bernstein Polynomials ◽  
Three Dimensional ◽  
Accurate Estimation ◽  
Interior Point Algorithm ◽  
Research Attention ◽  
Rossler System ◽  
Rössler System ◽  
Error Dynamics

Chaotic systems have gained enormous research attention since the pioneering work of Lorenz. Rössler system stands among the extensively studied classical chaotic models. This paper proposes a technique based on Bernstein Polynomial Basis Function to convert the three-dimensional Rössler system of Ordinary Differential Equations (ODEs) into an error minimization problem. First, the properties of Bernstein Polynomials are applied to derive the fitness function of Rössler chaotic system. Second, in order to obtain the values of unknown Bernstein coefficients to optimize the fitness function, the problem is solved using two versatile algorithms from the family of Evolutionary Algorithms (EAs), Genetic Algorithm (GA) hybridized with Interior Point Algorithm (IPA) and Differential Algorithm (DE). For validity of the proposed technique, simulation results are provided which verify the global stability of error dynamics and provide accurate estimation of the desired parameters.

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