Exponential synchronization of stochastic fuzzy cellular neural networks with time delay in the leakage term and reaction-diffusion

Separate studies have been published on the stability of fuzzy cellular neural networks with time delay in the leakage term and synchronization issue of coupled chaotic neural networks with stochastic perturbation and reaction-diffusion effects. However, there have not been studies that integrate the two fields. Motivated by the achievements from both fields, this paper considers the exponential synchronization problem of coupled chaotic fuzzy cellular neural networks with stochastic noise perturbation, time delay in the leakage term and reaction-diffusion effects using linear feedback control. Lyapunov stability theory combining with stochastic analysis approaches are employed to derive sufficient criteria ensuring the coupled chaotic fuzzy neural networks to be exponentially synchronized. This paper also presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed scheme.     

Quantized adaptive pinning control for fixed/preassigned-time cluster synchronization of multi-weighted complex networks with stochastic disturbances

The article studies preassigned-time (PAT) and fixed-time (FXT) cluster synchronization of multi-weighted complex networks (CNs) with stochastic disturbances through designing quantized adaptive pinning control scheme. Firstly, the controller can achieve lower control costs and save communication channels. By designing a novel and appropriate Lyapunov function, combining the characteristics of Wiener process and utilizing a method of comparison system, FXT synchronization criteria of CNs are proposed. The FXT criteria can be more widely applied in directed and undirected multi-weighted CNs. Besides, the PAT cluster synchronization is also investigated by utilizing a novel control scheme that the gains of controller are finite, where the synchronization time can be preassigned based on realistic situation. The effectiveness of theoretical results is illustrated via simulations.     

Exponential stability of reaction-diffusion high-order Markovian jump Hopfield neural networks with time-varying delays

This paper studies the problems of global exponential stability of reaction-diffusion high-order Markovian jump Hopfield neural networks with time-varying delays. By employing a new Lyapunov-Krasovskii functional and linear matrix inequality, some criteria of global exponential stability in the mean square for the reaction-diffusion high-order neural networks are established, which are easily verifiable and have a wider adaptive. An example is also discussed to illustrate our results.     

Exponential synchronization of fuzzy cellular neural networks with mixed delays and general boundary conditions

In this paper, we study exponential synchronization of delayed reaction-diffusion fuzzy cellular neural networks with general boundary conditions. By using Sobolev inequality techniques and constructing suitable Lyapunov functional, some sufficient conditions are given to ensure the exponential synchronization of the drive-response delayed fuzzy cellular neural networks with general boundary conditions. Finally, an example is given to verify the theoretical analysis.     

Exponential synchronization of stochastic perturbed complex networks with time-varying delays via periodically intermittent pinning

In this paper, the exponential synchronization is investigated for stochastic complex networks with time-varying delays via periodically intermittent pinning control. By utilizing the Lyapunov stability theory, stochastic analysis theory as well as linear matrix inequalities (LMI), the sufficient conditions are derived to guarantee the exponential synchronization. Furthermore, the complex networks considered in this paper are more general than the models in previous works. Therefore, the application scope is enlarged. And the result is computationally efficient for the obtained condition. The numerical simulation is provided to show the effectiveness of the theoretical results.     

Robust global synchronization of complex networks with neutral-type delayed nodes

In this paper, the problems of robust global exponential synchronization for a class of complex networks with time-varying delayed couplings are considered. Each node in the network is composed of a class of delayed neural networks described by a nonlinear delay differential equation of neutral-type. Since model errors commonly exist in practical applications, the parameter uncertainties are involved in the considered model. Sufficient conditions that ensure the complex networks to be robustly globally exponentially synchronized are obtained by using the Lyapunov functional method and some properties of Kronecker product. An illustrative example is presented to show the effectiveness of the proposed approach.     

Exponential synchronization of complex dynamical networks with markovian jump parameters and stochastic delays and its application to multi-agent systems

This paper studies the synchronization problem of complex dynamical networks with stochastic delay which switches stochastically among several forms of time-varying delays. Both the discrete and distributed delays are considered, as well as the Markovian jump parameters. The occurrence probability distribution of the stochastic delay is assumed to be known in prior. By utilizing the Lyapunov–Krasovskii stability theory and stochastic analysis techniques, some sufficient exponential synchronization criteria are obtained, which depend not only on the size of delays, but also on the occurrence probability distribution of the stochastic delay. Moreover, the main results are successfully extended to multi-agent systems with stochastic delay. Several numerical examples are given to illustrate the feasibility and effectiveness of the proposed methods.     

Exponential synchronization of stochastic coupled oscillators networks with delays

This paper investigates the exponential synchronization problem of coupled oscillators networks with disturbances and time-varying delays. On basis of graph theory and stochastic analysis theory, a feedback control law is designed to achieve exponential synchronization. By constructing a global Lyapunov function for error network, both pth moment exponential synchronization and almost sure exponential synchronization of drive-response networks are obtained. Finally, a numerical example is given to show the effectiveness of the proposed criteria.     

Global exponential stability of nonautonomous neural networks with time-varying delays and reaction-diffusion terms

A class of nonautonomous neural networks with time-varying delays and reaction-diffusion terms is considered. By means of Lyapunov functionals and differential inequality techniques, criteria on global exponential stability of this model with the Neumann boundary conditions and the Dirichlet boundary conditions are derived, respectively. The results of this paper are new and they improve and generalize previously known results.     

Synchronization for delayed non-autonomous reaction-diffusion fuzzy cellular neural networks

In this letter, the synchronization schemes for delayed non-autonomous reaction-diffusion fuzzy cellular neural networks is considered. Based on the simple adaptive controller, a set of sufficient conditions to guarantee the synchronization are obtained. Moreover, the asymptotic behavior of the unknown parameters can be derived in the meanwhile. At last, some examples are given to show the effectiveness of the main results.     

Pinning sampled‐data synchronization of complex dynamical networks with Markovian jumping and mixed delays using multiple integral approach

This study deals with the pinning synchronization problem for complex dynamical networks (CDNs) with Markovian jumping parameters and mixed delays under sampled‐data control technique. The mixed delays cover both discrete and distributed delays. The Markovian jumping parameters are modeled as a continuous‐time, finite‐state Markov chain. The sufficient conditions for asymptotic synchronization of considered networks are obtained by utilizing novel Lyapunov‐Krasovskii functional and multiple integral approach. The obtained criteria is formulated in terms of LMIs, which can be checked for feasibility by making use of available softwares. Lastly, numerical simulation results are presented to validate the advantage of the propound theoretical results. © 2016 Wiley Periodicals, Inc. Complexity 21: 622–632, 2016     

复杂动态网络的有限时间同步

复杂网络无处不在,同步是自然界中广泛存在的一类非常重要的非线性现象.过去10年,人们对复杂网络的同步开展了系统而深入的研究,包括恒等同步、广义同步、簇同步以及部分同步等.上述大部分结果中对同步速度的刻画往往是渐进的,只有当时间趋于无穷的时候,网络才能实现同步,而对于网络能够在多长时间内可以实现同步却知之甚少.作者以几类典型的非线性耦合的复杂动态网络为例,深入探讨了复杂动态网络的有限时间同步的规律.具体而言,基于上述几类典型的复杂动态网络,证明了在某些合适的条件下,网络能够在有限时间内实现精确同步.此外,用一个典型的数值仿真实例验证了上述有限时间同步的准则.有限时间同步有效地避免了网络只有在无穷时刻才能实现同步的问题,对网络同步的实际工程应用具有基本的现实意义.     

On periodic solutions to a class of non-autonomously delayed reaction-diffusion neural networks

In this paper, we investigate the existence and attractivity of periodic solutions for non-autonomous reaction-diffusion Cohen–Grossberg neural networks with discrete time delays. By combining the Lyapunov functional method with the contraction mapping principle and Poincaré inequality, we establish several criteria for the existence and global exponential stability of periodic solutions. More interestingly, Poincaré inequality is used to handle the reaction-diffusion terms, hence all the criteria depend on reaction-diffusion terms. These criteria are applicable in Cohen–Grossberg neural networks with both the Dirichlet and the Neumann boundary conditions on a general space domain. Several examples with numerical simulations are given to demonstrate the results.     

Dupire It\^{o}’s formula for the exponential synchronization of stochastic semi-Markov jump systems with mixed delay under impulsive control

This paper emphasizes the exponential synchronization for a class of stochastic semi-Markov jump systems with mixed delay via stochastic hybrid impulsive control. The impulsive sequence includes synchronous and asynchronous impulses with the impulsive gains being a sequence of stochastic variables. Inspired by the idea of average, a concept of ``average stochastic impulsive gain" is used to qualify the impulse intensity. Our approach expands Dupire functional It\^{o}$"s formula to the stochastic semi-Markov jump systems with mixed delay for the first time. Moreover, in view of the established Lyapunov functional, graph theory, and stochastic analysis theory, some exponential synchronization criteria for the systems are derived. The theoretical results are applied to a class of Chua"s circuit systems with semi-Markov jump and mixed delay. Some synchronization criteria for the circuit systems are provided. The simulation results verify the effectiveness of the theoretical results.     

Adaptive synchronization in an array of chaotic neural networks with mixed delays and jumping stochastically hybrid coupling

In this paper, a general model of an array of N linearly coupled delayed neural networks with Markovian jumping hybrid coupling is introduced. The hybrid coupling consists of constant coupling, discrete and distributed time-varying delay coupling. The complex dynamical network jumps from one mode to another according to a Markovian chain, where all the coupling configurations are also dependent on mode switching. Meanwhile, all the coupling terms are subjected to stochastic disturbances which are described in terms of a Brownian motion. By adaptive approach, some sufficient criteria have been derived to ensure the synchronization in an array of jump neural networks with mixed delays and hybrid coupling in mean square. Surprisingly, it is found that complex networks with two different structure can also be synchronized according to known probability matrix. Finally, an example illustrated by switching between small-world networks and nearest-neighbor networks is given to show the effectiveness of the proposed criteria.     

Observer-based lag synchronization between two different complex networks

In this paper, some new criteria for lag synchronization between two or more complex networks are proposed based on the theory of state observer. Some adaptive controllers are designed to make the drive and response systems achieve lag synchronization, no matter whether the nodes in the two systems are with the same dynamical character or the coupling configuration matrices are nonidentical. In addition, based on the output coupling, the amount of coupling variables between two connected nodes is flexible, which can save a lot of channel resources, simplify the network topology and has more significant meanings in engineering applications. At last, the effects of the lag synchronization criteria are verified through some simulation experiments.     

SYNCHRONIZATION OF SINGULAR MARKOVIAN JUMPING NEUTRAL COMPLEX DYNAMICAL NETWORKS WITH TIME-VARYING DELAYS VIA PINNING CONTROL

This article discusses the synchronization problem of singular neutral complex dynamical networks(SNCDN) with distributed delay and Markovian jump parameters via pinning control. Pinning control strategies are designed to make the singular neutral complex networks synchronized. Some delay-dependent synchronization criteria are derived in the form of linear matrix inequalities based on a modified Lyapunov-Krasovskii functional approach. By applying the Lyapunov stability theory, Jensen's inequality, Schur complement,and linear matrix inequality technique, some new delay-dependent conditions are derived to guarantee the stability of the system. Finally, numerical examples are presented to illustrate the effectiveness of the obtained results.     

Influence of noise and delay on reaction-diffusion recurrent neural networks

In this paper, the influence of the noise and delay upon the stability property of reaction-diffusion recurrent neural networks （RNNs） with the time-varying delay is discussed. The new and easily verifiable conditions to guarantee the mean value exponential stability of an equilibrium solution are derived. The rate of exponential convergence can be estimated by means of a simple computation based on these criteria.     

Synchronization of delayed coupled reaction‐diffusion systems on networks

In this paper, the exponential synchronization problem of delayed coupled reaction‐diffusion systems on networks (DCRDSNs) is investigated. Based on graph theory, a systematic method is designed to achieve exponential synchronization between two DCRDSNs by constructing a global Lyapunov function for error system. Two different kinds of sufficient synchronization criteria are derived in the form of Lyapunov functions and coefficients of drive‐response systems, respectively. Finally, a numerical example is given to show the usefulness of the proposed criteria. Copyright © 2014 John Wiley & Sons, Ltd.