Chaotic synchronization via linear controller

This paper presents a new method to synchronize different chaotic systems with disturbances via an active radial basis function (RBF) sliding controller. This method incorporates the advantages of active control, neural network and sliding mode control. The main part of the controller is given based on the output of the RBF neural networks and the weights of these single layer networks are tuned on-line based on the sliding mode reaching law. Only several radial basis functions are required for this controller which takes the sliding mode variable as the only input. The proposed controller can make the synchronization error converge to zero quickly and can overcome external disturbances. Analysis of the stability for the controller is carried out based on the Lyapunov stability theorem. Finally, five examples are given to illustrate the robustness and effectiveness of the proposed synchronization control strategy.

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Synchronization of different chaotic systems via active radial basis functions sliding mode controller

Hui-Jun

You-Wei

Wang

2008

Chinese Phys. B

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The synchronization of FitzHugh–Nagumo neuron network coupled by gap junction

Ye¹,

Zhang²,

Zhao³

et al. 2008

Chinese Phys. B

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It is well known that the strong coupling can synchronize a network of nonlinear oscillators. Synchronization provides the basis of the remarkable computational performance of the brain. In this paper the FitzHugh–Nagumo neuron network is constructed. The dependence of the synchronization on the coupling strength, the noise intensity and the size of the neuron network has been discussed. The results indicate that the coupling among neurons works to improve the synchronization, and noise increases the neuron random dynamics and the local fluctuations; the larger the size of network, the worse the synchronization. The dependence of the synchronization on the strength of the electric synapse coupling and chemical synapse coupling has also been discussed, which proves that electric synapse coupling can enhance the synchronization of the neuron network largely.

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Analysis of convergence for initial condition estimation of coupled map lattices based on symbolic dynamics

孙丽莎¹,

康晓云²,

林兰馨³

2010

Chinese Phys. B

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A novel approach to the inverse problem of diffusively coupled map lattices is systematically investigated by utilizing the symbolic vector dynamics. The relationship between the performance of initial condition estimation and the structural feature of dynamical system is proved theoretically. It is found that any point in a spatiotemporal coupled system is not necessary to converge to its initial value with respect to sufficient backward iteration, which is directly relevant to the coupling strength and local mapping function. When the convergence is met, the error bound in estimating the initial condition is proposed in a noiseless environment, which is determined by the dimension of attractors and metric entropy of the system. Simulation results further confirm the theoretic analysis, and prove that the presented method provides the important theory and experimental results for better analysing and characterizing the spatiotemporal complex behaviours in an actual system.

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Chaotic synchronization via linear controller

Cited by 9 publications

References 21 publications

Synchronization of different chaotic systems via active radial basis functions sliding mode controller

Synchronization of different chaotic systems via active radial basis functions sliding mode controller

The synchronization of FitzHugh–Nagumo neuron network coupled by gap junction

Analysis of convergence for initial condition estimation of coupled map lattices based on symbolic dynamics

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