Synchronization control of stochastic delayed neural networks

In this paper, synchronization control of stochastic neural networks with time-varying delays has been considered. A novel control method is given using the Lyapunov functional method and linear matrix inequality (LMI) approach. Several sufficient conditions have been derived to ensure the global asymptotical stability in mean square for the error system, and thus the drive system synchronize with the response system. Also, the estimation gains can be obtained. With these new and effective methods, synchronization can be achieved. Simulation results are given to verify the theoretical analysis in this paper.     

Synchronization analysis of coloured delayed networks under decentralized pinning intermittent control

This paper investigates synchronization of coloured delayed networks under decentralized pinning intermittent control. To begin with, the time delays are taken into account in the coloured networks. In addition, we propose a decentralized pinning intermittent control are method, which is different from that most of pinning intermittent controls are only applied to the nodes from 1 to l or centralized nodes. Moreover, sufficient conditions are derived to guarantee the synchronization of coloured delayed networks based on Lyapunov stability theorem. Finally, numerical simulations are provided to verify the validity of the obtained results.     

Synchronization of delayed fuzzy cellular neural networks based on adaptive control

This Letter studies synchronization of delayed fuzzy cellular neural networks with all the parameters unknown. To enhance the coupled strength dynamically and be more suitable for the reality, we add fuzzy theory to the traditional cellular neural networks. By the Lyapunov-Lasall principle of functional differential equations, some new stability criteria are obtained via adaptive control. To the best of our knowledge, there has few work studying fuzzy cellular neural networks. Moreover, the approaches developed here extend the ideas and techniques derived in recent literatures. In the end, an example and its simulation were given to illustrate the simpleness and effectiveness of our main results.     

时滞离散神经网络的同步控制

利用既有效又便于实施的时滞状态反馈控制器,根据所给定的条件构造相应的不等式,研究了带有时滞的离散神经网络模型的同步控制问题,给出了该离散系统指数同步的充分条件.在设计同步控制的时候,没有假设激励函数的有界性、可微性和单调性,给出的条件简便易实施.数值结果进一步证明了该控制方法的有效性. 关键词： 离散神经网络 时滞 同步     

Taming instabilities in power grid networks by decentralized control

Renewables will soon dominate energy production in our electric power system. And yet, how to integrate renewable energy into the grid and the market is still a subject of major debate. Decentral Smart Grid Control (DSGC) was recently proposed as a robust and decentralized approach to balance supply and demand and to guarantee a grid operation that is both economically and dynamically feasible. Here, we analyze the impact of network topology by assessing the stability of essential network motifs using both linear stability analysis and basin volume for delay systems. Our results indicate that if frequency measurements are averaged over sufficiently large time intervals, DSGC enhances the stability of extended power grid systems. We further investigate whether DSGC supports centralized and/or decentralized power production and find it to be applicable to both. However, our results on cycle-like systems suggest that DSGC favors systems with decentralized production. Here, lower line capacities and lower averaging times are required compared to those with centralized production.     

Synchronization of relaxational self-oscillating systems: Synchronization in neural networks

A general problem of the synchronization and mutual synchronization of relaxational self-oscillating systems is formulated. A direct way of describing the synchronization of relaxational systems on the basis of Kronecker’s inequalities is proposed. The solution to the problem formulated by N. Wiener and A. Rosenbluth of forming a single rhythm in a system of coupled relaxational oscillators is described. Specific transient processes in the synchronization of relaxational systems are considered. Burst synchronization in neural networks and synchronization in distributed relaxational systems are also described.     

Memory feedback finite-time control for memristive neutral-type neural networks with quantization

The drive-response of memory feedback control design for memristor neural networks of neutral type over finite-time domain is scrutinized in this paper. Notably, the main purpose of this work is to synthesize memory feedback controller in the presence of logarithmic quantizer and actuator saturation to guarantee the finite-time boundedness of the resulting memristive neural networks. On basis of proper Lyapunov–Krasovskii-functional and linear matrix inequalities, new sufficient criterian is established to assure the delay-dependent finite-time stabilization criteria for the addressed network model. Also, by solving the developed linear matrix inequalities, the finite-time memory feedback control law gain matrices could be attained. Eventually, the validations of the proposed mechanism are ultimately explored through two numerical examples.     

混沌神经网络的延时反馈控制研究

针对混沌神经网络，提出了一种改进的延时反馈控制方法. 利用该方法，当延时参数τ为奇数时，被控神经网络收敛于记忆模式以及它的反相模式的2周期上. 若选取不同的延时参数，被控网络则收敛于不同的周期态上. 关键词： 控制混沌 延时反馈控制 混沌神经网络     

基于反馈精确线性化的混沌系统同步控制方法

应用精确线性化方法，通过严格的状态变换和反馈方法，将非线性混沌系统线性化. 考虑到系统的部分状态变量无法测量，设计了混沌系统的状态观测器，求解出了状态观测器的反馈控制律. 将这种控制方法应用于Lorenz混沌系统的同步控制，仿真结果表明，系统三个状态变量的同步误差均能在很短的时间内收敛到零.因此，该同步控制方法在保证闭环系统稳定的前提下，具有较好的同步控制快速性和较高的控制精度. 关键词： 反馈线性化 混沌同步 状态观测器     

Synchronization and coherence resonance in chaotic neural networks

Synchronization and coherence of chaotic Morris--Lecar (ML) neural networks have been investigated by numerical methods. The synchronization of the neurons can be enhanced by increasing the number of the shortcuts, even though all neurons are chaotic when uncoupled. Moreover, the coherence of the neurons exhibits a non-monotonic dependence on the density of shortcuts. There is an optimal number of shortcuts at which the neurons' motion is most ordered, i.e. the order parameter (the characteristic correlation time) that is introduced to measure the coherence of the neurons has a maximum. These phenomena imply that stochastic shortcuts can tame spatiotemporal chaos. The effects of the coupling strength have also been studied. The value of the optimal number of shortcuts goes down as the coupling strength increases.     

混沌神经网络逆控制的同步及其在保密通信系统中的应用

利用神经网络的学习、逼近能力构造混沌神经网络，提出逆控制混沌同步方法来同步两个混沌神经网络，并基于逆控制和混沌神经网络的同步给出一种新的混沌保密通信系统.理论分析和数值实验结果表明，新系统能够有效地克服信道噪声对信息传输的不良影响，具有较强通用性和柔韧性，且有同步速度快，信号恢复精度高和密钥量大的优点. 关键词： 混沌同步 自适应逆控制 混沌神经网络 保密通信     

Impulsive effect on exponential synchronization of neural networks with leakage delay under sampled-data feedback control

We consider the impulsive effect on the exponential synchronization of neural networks with leakage delay under the sampled-data feedback control. We use an appropriate Lyapunov–Krasovskii functional combined with the input delay approach and some inequality techniques to derive sufficient conditions that ensure the exponential synchronization of the delayed neural network. The conditions are formulated in terms of the leakage delay, the sampling period, and the exponential convergence rate. Numerical examples are given to demonstrate the usefulness and the effectiveness of the results.     

不确定混沌系统的回归神经网络同步

将高阶连接的神经元融合到分布式回归神经网络，研究了出现非模型动态性时不确定混沌系统的辨识和同步问题。采用李雅谱诺夫稳定理论对高阶神经网络回归模型的权值进行学习更新，同时，获取整个系统稳定特性的分析结果，而且通过李雅谱诺夫方法设计出消除不确定混沌系统的同步误差的自适应控制律。最后将所提出的方法应用到不确定Rossler混沌系统的建模与同步     

Synchronization criteria for coupled Hopfield neural networks with time-varying delays

This paper proposes new delay-dependent synchronization criteria for coupled Hopfield neural networks with time-varying delays. By construction of a suitable Lyapunov-Krasovskii's functional and use of Finsler's lemma, novel synchronization criteria for the networks are established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Two numerical examples are given to illustrate the effectiveness of the proposed methods.     

Synchronization of Markovian jumping complex networks with event-triggered control

This paper investigates event-triggered synchronization for complex networks with Markovian jumping parameters.Nonlinear dynamics with Markovian jumping parameters is considered for each node in a complex network. By utilizing the proposed event-triggered strategy, and based on the Lyapunov functional method and linear matrix inequality technology,some sufficient conditions for synchronization of complex networks are derived whether the transition rate matrix for the Markov process is completely known or not. Finally, a numerical example is presented to illustrate the effectiveness of the proposed theoretical results.     

Synchronization of general complex networks via adaptive control schemes

In this paper, the synchronization problem of general complex networks is investigated by using adaptive control schemes. Time-delay coupling, derivative coupling, nonlinear coupling etc. exist universally in real-world complex networks. The adaptive synchronization scheme is designed for the complex network with multiple class of coupling terms. A criterion guaranteeing synchronization of such complex networks is established by employing the Lyapunov stability theorem and adaptive control schemes. Finally, an illustrative example with numerical simulation is given to show the feasibility and efficiency of theoretical results.     

Synchronization between two different chaotic systems with nonlinear feedback control

This paper presents chaos synchronization between two different chaotic systems by using a nonlinear controller, in which the nonlinear functions of the system are used as a nonlinear feedback term. The feedback controller is designed on the basis of stability theory, and the area of feedback gain is determined. The artificial simulation results show that this control method is commendably effective and feasible.     

Stabilization of a class of dynamical complex networks based on decentralized control

This paper deals with a stabilization problem for a class of dynamical complex networks with each node being a general Lur’e system. Based on a Lur’e-Postnikov function and a special decentralized control strategy, the problem of designing a linear feedback controller such that states of all nodes are globally stabilized onto an expected homogeneous state is addressed. A controller design method based on parameter-dependent Lur’e-Postnikov function is proposed in order to reduce the conservativeness and the controller can be constructed via feasible solutions of a certain set of linear matrix inequalities (LMIs). A dynamical network composed of identical Chua's circuits is adopted as a numerical example to demonstrate the effectiveness of the proposed results.     

Synchronization of networks

We study the synchronization of coupled dynamical systems on networks. The dynamics is governed by a local nonlinear oscillator for each node of the network and interactions connecting different nodes via the links of the network. We consider existence and stability conditions for both single- and multi-cluster synchronization. For networks with time-varying topology we compare the synchronization properties of these networks with the corresponding time-average network. We find that if the different coupling matrices corresponding to the time-varying networks commute with each other then the stability of the synchronized state for both the time-varying and the time-average topologies are approximately the same. On the other hand, for non-commuting coupling matrices the stability of the synchronized state for the time-varying topology is in general better than the time-average topology.