Isochronal function projective synchronization between chaotic and time-delayed chaotic systems

Wu, Ranchao; Liu, Junlian

doi:10.1186/1687-1847-2012-37

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Chaos synchronization has attracted a great deal of attention ever since Pecora and Carroll [1] established a chaos synchronization scheme for two identical chaotic systems with different initial conditions. Many methods for chaos synchronization have been proposed, such as, Robust Control [2], the sliding method control [3], linear and nonlinear feedback control [4], function projective [5,6], adaptive control [7], active control [8], backstepping control [9], generalized backsteppig method control [10] and so on. But many above-mentioned methods can only applied some given chaotic system, some methods will produce the singularity problem in synchronizing the chaotic system and most of the methods in the literatures need more than one variable information of the master system.…”

Section: Introductionmentioning

confidence: 99%

Synchronization New 3D Chaotic System Using Brain Emotional Learning Based Intelligent Controller

Ziabari¹,

Sahab²,

Fakhari³

2015

IJITCS

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One of the most important phenomena of some systems is chaos which is caused by nonlinear dynamics. In this paper, the new 3 dimensional chaotic system is firstly investigated and then utilizing an intelligent controller which based on brain emotional learning (BELBIC), this new chaotic system is synchronized. The BELBIC consists of reward signal which accept positive values. Improper selection of the parameters causes an improper behavior which may cause serious problems such as instability of system. It is needed to optimize these parameters. Genetic Algorithm (GA), Cuckoo Optimization Algorithm (COA), Particle Swarm Optimization Algorithm (PSO) and Imperialist Competitive Algorithm (ICA) are used to compute the optimal parameters for the reward signal of BELBIC. These algorithms can select appropriate and optimal values for the parameters. These minimize the Cost Function, so the optimal values for the parameters will be founded. Selected cost function is defined to minimizing the least square errors. Cost function enforce the system errors to decay to zero rapidly. Numerical simulation results are presented to show the effectiveness of the proposed method.

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