不同节点动态的复杂网络外部同步牵制控制

研究节点动态不同的两个复杂网络的外部同步问题。运用牵制控制方法,网络模型选取节点输出线性耦合模型,基于输出控制思想,设计结构简单的牵制控制器,对响应网络中的部分节点施加输出反馈控制,使得两个复杂动态网络达到外部同步,即实现响应网络与驱动网络的渐近同步。根据李雅普诺夫稳定性理论,推出相应的同步准则,得到控制器参数选择条件。仿真时,驱动网络和响应网络分别选取Lorenz系统和Lu系统,对全局耦合网络和最近邻耦合网络两个典型网络拓扑进行仿真,验证了所提方法的有效性。     

EEG信号构建的复杂网络同步稳定性分析

人脑是自然界最复杂的系统之一,脑网络作为复杂网络理论在神经科学中的重要应用,为脑疾病的病理机制提供了新的研究方向。同步性作为影响网络性能的指标,对于复杂网络有着重要影响。为了研究同步性在脑网络中的表现,利用EEG动力学方程对120例酗酒病人的EEG信号进行复杂网络模型构造,根据所构造的模型利用李雅普诺夫稳定性理论进行证明。并通过实验对正常人和酗酒者脑网络同步状态做出统计,给出了酗酒病人和正常人的脑网络同步差异,可以揭示酗酒疾病对于人脑在功能结构上的影响,对其他疾病提供研究思路。     

不同节点动态的复杂网络外部同步牵制控制

研究节点动态不同的两个复杂网络的外部同步问题。运用牵制控制方法,网络模型选取节点输出线性耦合模型,基于输出控制思想,设计结构简单的牵制控制器,对响应网络中的部分节点施加输出反馈控制,使得两个复杂动态网络达到外部同步,即实现响应网络与驱动网络的渐近同步。根据李雅普诺夫稳定性理论,推出相应的同步准则,得到控制器参数选择条件。仿真时,驱动网络和响应网络分别选取Lorenz系统和Lü系统,对全局耦合网络和最近邻耦合网络两个典型网络拓扑进行仿真,验证了所提方法的有效性。     

复杂网络聚类方法

网络簇结构是复杂网络最普遍和最重要的拓扑属性之一,具有同簇节点相互连接密集、异簇节点相互连接稀疏的特点.揭示网络簇结构的复杂网络聚类方法对分析复杂网络拓扑结构、理解其功能、发现其隐含模式、预测其行为都具有十分重要的理论意义,在社会网、生物网和万维网中具有广泛应用.综述了复杂网络聚类方法的研究背景、研究意义、国内外研究现状以及目前所面临的主要问题,试图为这个新兴的研究方向勾画出一个较为全面和清晰的概貌,为复杂网络分析、数据挖掘、智能Web、生物信息学等相关领域的研究者提供有益的参考.     

一类不确定离散时间系统的鲁棒控制

本文针对一类离散时间不确定多输入系统,采用李雅普诺夫第二方法设计了一种鲁棒的线性状态反馈控制器.分析了控制系统的稳定性,并得出系统稳定的充分条件.最后给出仿真例子.     

基于仿射Bessel-Legendre不等式和不确定转移率的神经网络稳定性

针对具有时变时滞和不确定转移率的马尔科夫神经网络系统,充分考虑马尔科夫转移率的不确定特性,利用基于松弛变量的有效技术代替传统不等式来约束转移速率中的不确定项,从而减少了决策变量的个数并降低了计算复杂度.通过建立时滞依赖的增广Lyapunov-Krasovskii泛函,并基于仿射Bessel-Legendre(B-L)不等式,给出依赖于时滞和时滞导数上下界的具有较小保守性的神经网络系统稳定条件.最后,通过两个数值例子说明了理论结果的有效性.     

高维不确定数据高效聚类算法

维度灾难、含有噪声数据和输入参数对领域知识的强依赖性,是不确定数据聚类领域中具有挑战性的问题。针对这些问题,基于相似性度量和凝聚层次聚类思想的基础上提出了高维不确定数据高效聚类HDUDEC（High Dimensional Un-certain Data Efficient Clustering）算法。该算法采用一个能够准确表达不确定高维对象之间的相似度的度量函数计算出对象之间的相似度,然后根据相似度阈值自底向上进行聚类分析。实验证明新的算法需要的先验知识较少、可以有效地过滤噪声数据、可以高效的获得任意形状的高维不确定聚类结果。     

高维不确定数据高效聚类算法

维度灾难、含有噪声数据和输入参数对领域知识的强依赖性,是不确定数据聚类领域中具有挑战性的问题。针对这些问题,基于相似性度量和凝聚层次聚类思想的基础上提出了高维不确定数据高效聚类HDUDEC(High Dimensional Uncertain Data Efficient Clustering)算法。该算法采用一个能够准确表达不确定高维对象之间的相似度的度量函数计算出对象之间的相似度,然后根据相似度阈值自底向上进行聚类分析。实验证明新的算法需要的先验知识较少、可以有效地过滤噪声数据、可以高效的获得任意形状的高维不确定聚类结果。     

考虑执行器饱和的复杂动态网络鲁棒采样切换控制

随着计算机和网络技术的发展,复杂动态网络鲁棒采样切换控制受到广泛关注。探讨了在执行器饱和情况下不确定切换复杂动态网络的非周期采样控制问题。由于采样间隔上限受到停留时间的限制,采样数据控制器模式和系统模式之间的异步问题可能是由采样间隔内切换的子系统引起的。通过考虑无切换的采样间隔和有切换的采样间隔,分别构建了参数相关的李雅普诺夫闭环泛函。在平均停留时间的定义下,借助所构造的泛函,给出了不确定切换复杂动态网络误差系统的均方指数稳定性判据。此外,基于稳定性准则,针对执行器饱和情况下的不确定切换复杂动态网络,设计了全新的异步非周期采样控制器。根据提出的稳定性条件并采用非周期采样控制器设计方法,保证了不确定切换复杂动态网络与目标节点指数同步。最后,将利用蔡氏电路的算例与其他类似方法进行了对比,验证了所提方法的有效性,同时证明了利用所提方法得到的最大允许采样区间较大。     

一种不确定数据流聚类算法

提出了EMicro算法,以解决不确定数据流上的聚类问题.与现有技术大多仅考虑元组间的距离不同,EMicro算法综合考虑了元组之间的距离与元组自身不确定性这两个因素,同时定义新标准来描述聚类结果质量.还提出了离群点处理机制,系统同时维护两个缓冲区,分别存放正常的微簇与潜在的离群点微簇,以期得到理想的性能.实验结果表明,与现有工作相比,EMicro的效率更高,且效果良好.     

Stability and synchronization for Markovian jump neural networks with partly unknown transition probabilities

This paper addresses the problems of stability and synchronization for a class of Markovian jump neural networks with partly unknown transition probabilities. We first study the stability analysis problem for a single neural network and present a sufficient condition guaranteeing the mean square asymptotic stability. Then based on the Lyapunov functional method and the Kronecker product technique, the chaos synchronization problem of an array of coupled networks is considered. Both the stability and the synchronization conditions are delay-dependent, which are expressed in terms of linear matrix inequalities. The effectiveness of the developed methods is shown by simulation examples.     

Event‐triggered dissipative synchronization for Markovian jump neural networks with general transition probabilities

In this paper, dissipative synchronization problem for the Markovian jump neural networks with time‐varying delay and general transition probabilities is investigated. An event‐triggered communication scheme is introduced to trigger the transmission only when the variation of the sampled vector exceeds a prescribed threshold condition. The transition probabilities of the Markovian jump delayed neural networks are allowed to be known, or uncertain, or unknown. By employing delay system approach, a new model of synchronization error system is proposed. Applying the Lyapunov‐Krasovskii functional and integral inequality combining with reciprocal convex technique, a delay‐dependent criterion is developed to guarantee the stochastic stability of the errors system and achieve strict (Q,S,R)?α dissipativity. The event‐triggered parameters can be derived by solving a set of linear matrix inequalities. A numerical example is presented to illustrate the effectiveness of the proposed design method.     

Approximate synchronization of uncertain complex delayed networks with non-identical nodes

In this article, a delayed complex network model with non-identical nodes is established, where the mismatched uncertain parameters are involved in each node. The concept of approximate synchronization is proposed. A linear feedback controller is designed and a sufficient condition for boundedness of the synchronous error is presented in terms of linear matrix inequalities. A pseudoconvex optimization problem for minimizing the upper bound of the synchronous error is proposed. Furthermore, an iterative algorithm involving convex problem is designed to solve this pseudoconvex optimization problem and obtain the globally optimal solution, by which the approximate synchronization is achieved. At last, two numerical examples are given to show the effectiveness of the proposed criterion.     

Adaptive synchronization of uncertain coupled stochastic complex networks

In this paper, the problem of adaptive synchronization of uncertain coupled complex networks is investigated. Some controllers and adaptive laws are designed to ensure achieving synchronization of a general complex network model. In particular, synchronization of coupled stochastic networks subject to random perturbations is studied, with a referenced node introduced as the target node for synchronization. An example is simulated on delayed neural networks coupled in a small‐world network topology, which demonstrates the feasibility and effectiveness of the proposed adaptive control method. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Event-triggered synchronization control for complex networks with uncertain inner coupling

This paper is concerned with the event-triggered synchronization control problem for a class of complex networks with uncertain inner couplings. The uncertain inner coupling under consideration is characterized in terms of the interval matrix. In order to save the communication and computation resources, the event-based mechanism is adopted and the event-triggered synchronization control scheme is proposed for the complex networks. First, we transform the event-triggered synchronization control problem into the exponential stabilization problem for a new class of dynamical systems with multiple delays. Then, by employing the Lyapunov stability theory, we derive a sufficient condition under which the multi-delayed system is exponentially stable. Subsequently, a set of event-triggered synchronization controllers is designed in terms of the solution to a linear matrix inequality that can be solved effectively by using available software. Finally, a numerical simulation example is presented to show the effectiveness of the proposed event-triggered control scheme.     

Anti-windup design of uncertain discrete-time Markovian jump systems with partially known transition probabilities and saturating actuator

This paper carries out a study on the design of anti-windup gains for uncertain discrete-time Markovian jump systems subject to both actuator saturation and partially known transition probabilities. The parameter uncertainties appearing in both the state and input matrices are assumed to be time-varying and norm-bounded. Under the assumption that a set of linear dynamic output feedback controllers have been designed to stabilise the Markovian jump system in the absence of actuator saturation, anti-windup compensation gains are designed for maximising the domain of attraction of the closed-loop system with actuator saturation. Then, by solving a convex optimisation problem with constraints of a set of linear matrix inequalities, the anti-windup compensation gains are obtained. A simulation example is provided to illustrate the effectiveness of the proposed technique.     

Stability analysis for discrete delayed Markovian jumping neural networks with partly unknown transition probabilities

This paper addresses the analysis problem of asymptotic stability for a class of uncertain neural networks with Markovian jumping parameters and time delays. The considered transition probabilities are assumed to be partially unknown. The parameter uncertainties are considered to be norm-bounded. A sufficient condition for the stability of the addressed neural networks is derived, which is expressed in terms of a set of linear matrix inequalities. A numerical example is given to verify the effectiveness of the developed results.     

On robust stabilization of Markovian jump systems with uncertain switching probabilities

This brief paper is concerned with the robust stabilization problem for a class of Markovian jump linear systems with uncertain switching probabilities. The uncertain Markovian jump system under consideration involves parameter uncertainties both in the system matrices and in the mode transition rate matrix. First, a new criterion for testing the robust stability of such systems is established in terms of linear matrix inequalities. Then, a sufficient condition is proposed for the design of robust state-feedback controllers. A globally convergent algorithm involving convex optimization is also presented to help construct such controllers effectively. Finally, a numerical simulation is used to illustrate the developed theory.     

Finite-time lag synchronization for uncertain complex networks involving impulsive disturbances

Neural Computing and Applications - This paper focuses on the finite-time lag synchronization (FTLS) of uncertain complex networks involving impulsive disturbance effects. By designing two...     

Event-triggered sampled-data synchronization of complex networks with uncertain inner coupling and time-varying delays

This paper focuses on event-triggered sampled-data synchronization of uncertain complex networks with time-varying coupled delays. First of all, a discrete event-triggered sampled-data control scheme is adopted, which not only makes the state of the system be monitored in discrete time, but also the sampling information is effectively transmitted. The proposed event-triggered mechanism effectively prevents Zeno behavior. In addition, we also use some novel piecewise time-dependent Lyapunov–Krasovskii and Wirtinger inequality to handle the time-varying delays and parameter uncertainties of complex networks. Then, synchronization criteria are given for uncertain complex networks. Finally, the simulation results show that the control scheme can significantly reduce the number of transmitted signals while maintaining the uncertain complex networks synchronization.