一般耦合结构时变时滞复杂网络的同步准则

研究一般耦合结构时变时滞复杂网络的同步问题,取消了复杂网络外部耦合矩阵是对称且不可简约的约束.对于一般的外部耦合矩阵,考虑了具有复数特征根的情况,并对复数域上的向量进行相应的处理.基于Lyapunov稳定性理论,利用矩阵分解技巧及线性矩阵不等式方法,通过构造适当的Lyapunov泛函,得到了网络系统实现同步的充分条件.为验证所得结论的有效性,对网络同步问题进行了数值仿真.仿真结果显示本文的同步准则较已有研究具有更小的保守性.     

两个具有耦合时滞的分数阶复杂网络的延迟投影同步与参数辨识

研究两个具有耦合时滞的分数阶复杂网络的延迟投影同步与参数辨识的问题.首先,建立具有耦合时滞以及模型参数不确定分数阶复杂网络模型,并给出驱动网络与响应网络的延迟投影同步误差模型;其次,设计有效的控制器以及参数自适应律以实现两个网络之间的延迟投影同步与参数辨识,给出了实现延迟投影同步与参数辨识的充分条件,对该充分条件给出了...     

具有脉冲耦合复杂时滞动力网络的同步

考虑了一个具有脉冲耦合的复杂时滞动力网络的同步问题.基于脉冲时滞动力系统扩展的Halanay不等式,给出了网络同步的一个充分条件.所获结果表明,即使网络节点之间仅在一些离散时刻存在瞬时连接,网络仍然能够达到同步.进一步将所得结果应用于一个由混沌FHN神经元振子为动力节点所构成的一个无标度的动力网络,数值仿真结果表明了理...     

非线性耦合统一混沌系统的同步

研究非线性耦合的两个统一混沌系统的同步问题．首先利用线性时变系统的稳定性理论,推出当两个统一混沌系统的误差系统渐近稳定时,耦合函数的参数选择范围,从而得出两个统一混沌系统全局渐近同步的充分条件．然后基于Routh-Hurwitz稳定性判别方法,同样得出了混沌系统同步的一个充分条件．通过数值仿真发现,根据第1种方法选择的参数能使混沌系统全局渐近同步;而依据第2种方法选择的参数,即使误差系统系数矩阵的瞬间特征值具有负实部,也会出现混沌同步失去的情况,从而表明了该分析方法的有效性．     

分数阶复杂网络的混合投影同步研究

主要针对一类节点为分数阶混沌系统的复杂网络混合投影同步进行研究.基于分数阶系统的稳定性理论和非线性反馈控制方法,通过设计有效的控制器,实现了不同节点的复杂网络的混合投影同步,并给出了实现投影同步的充分条件,不仅从理论上分析了该控制器可以使复杂网络系统实现投影同步,而且大量的数值模拟证明所设计控制器的正确性和有效性.     

噪声下相互依存网络的自适应H∞异质同步

针对具有噪声的相互依存复杂动力网络, 本文研究了它的局部自适应H_∞异质同步问题.该网络由两个具有"一对一"相互依赖关系的子网构成, 子网内部耦合和子网间的耦合均含有未知但有界的非线性函数.基于李雅普诺夫稳定性理论、线性矩阵不等式(Linear matrix inequality, LMI)技术和自适应以及H_∞控制方法, 本文提出了使得相互依存网络在外部噪声的干扰下, 两个子网各自达到一致的充分条件.这些条件不仅可以保证受扰动的网络获得鲁棒渐近同步而且可以让网络达到一个给定的鲁棒H_∞水平.最后的数值模拟验证了提出的方法的有效性以及可行性.     

基于非线性牵制控制的分数阶混沌网络聚同步

针对分数阶混沌复杂网络,提出一种非线性牵制控制策略实现网络聚同步.根据网络结点的不同属性,只对群间点施加非线性控制,然后基于分数阶系统稳定性理论,给出了实现聚同步的充分条件.数值仿真验证了该聚同步方案的有效性和正确性,同时深入讨论了控制增益和耦合强度等对聚同步的影响.     

两个分数阶复杂动态网络的函数投影同步

复杂网络的同步问题是复杂网络研究的热点之一, 本文研究了两个分数阶复杂网络间的函数投影同步问题.分别针对网络模型参数已知和参数未知两种情况, 利用自适应控制技术和分数阶系统稳定性理论, 设计自适应控制器, 使两个分数阶复杂动态网络实现函数投影同步.最后利用数值仿真验证所提出方法的有效性.     

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。     

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。     

基于非线性观测器设计的混沌同步控制

提出一种基于非线性观测器设计的混沌同步控制方法,给出了一类含有不确定参数混沌系统的观测器实现条件.该控制策略可满足混沌系统中存在一些不确定参数的要求.通过对Lorenz混沌系统的理论分析和数字仿真,证明了其有效性.     

Exponential synchronization of two totally different chaotic systems based on a unified model

This paper presents an exponential synchronization scheme between two chaotic systems with different structures and parameters. A unified model consisting of a linear dynamic system and a bounded static nonlinear operator is employed to describe these totally different chaotic systems. A novel state feedback control law is established to exponentially synchronize the two unified models with different parameters. Most chaotic systems with different structures and parameters, such as Hopfield neural networks, cellular neural networks, Chua’s circuits, unified chaotic systems, Qi systems, and chaotic recurrent multilayer perceptrons, can be transformed into this unified model with the synchronization controller designed in a unified way. Two numerical examples are exploited to illustrate the effectiveness of the proposed design schemes.     

Lag stochastic synchronization of chaotic mixed time-delayed neural networks with uncertain parameters or perturbations

This paper investigates the problem of lag synchronization for a kind of chaotic neural networks with discrete and distributed delays (mixed delays). The driver system has uncertain parameters and uncertain nonlinear external perturbations, while the response system has channel noises. A simple but all-powerful robust adaptive controller is designed to circumvent the effects of uncertain external perturbations such that the response system synchronize with the driver system. Based on the invariance principle of stochastic differential equations and some suitable Lyapunov functions, several sufficient conditions are developed to solve this problem. Moreover, under certain conditions, parameters of the uncertain master system can be estimated. Numerical simulations are exploited to show the effectiveness of the theoretical results.     

一类三维动力系统的同步控制与仿真

针对一类三维动力系统,提出了一种同步控制法。基于李雅谱诺夫稳定性理论设计了一简单的自适应反馈控制器来实现同步控制。理论证明和数值模拟的结果都验证了控制器的有效性。控制器简单便于实现,更可以用于一般非线性混沌系统的控制。     

时滞社团网络联合同步

本文提出了一种全新的多目标混沌系统和时滞社团网络间的联合同步类型,在考虑噪声影响的情况下,设计了联合滑模控制器控制时滞社团网络,修正了主动滑模控制法,得到了时滞社团网络的同步律,并通过数值模拟实现了多目标非线性系统的联合同步,验证了多目标联合驱动的有效性.     

Generalized and partial synchronization of coupled neural networks

Synchronization of neural signals has been proposed as a temporal coding scheme representing cooperated computation in distributed cortical networks. Previous theoretical studies in that direction mainly focused on the synchronization of coupled oscillatory subsystems and neglected more complex dynamical modes, that already exist on the single-unit level. In this paper we study the parametrized time-discrete dynamics of two coupled recurrent networks of graded neurons. Conditions for the existence of partially synchronized dynamics of these systems are derived, referring to a situation where only subsets of neurons in each sub-network are synchronous. The coupled networks can have different architectures and even a different number of neurons. Periodic as well as quasiperiodic and chaotic attractors constrained to a manifold M of synchronized components are observed. Examples are discussed for coupled 3-neuron networks having different architectures, and for coupled 2-neuron and 3-neuron networks. Partial synchronization of different degrees is demonstrated by numerical results for selected sets of parameters. In conclusion, the results show that synchronization phenomena far beyond completely synchronized oscillations can occur even in simple coupled networks. The type of the synchronization depends in an intricate way on stimuli, history and connectivity as well as other parameters of the network. Specific inputs can further switch between different operational modes in a complex way, suggesting a similarly rich spatio-temporal behaviour in real neural systems.     

Stable indirect adaptive interval type-2 fuzzy sliding-based control and synchronization of two different chaotic systems

An indirect approach to adaptive interval type-2 fuzzy sliding mode control is proposed for the stable synchronization of two different chaotic nonlinear systems with different initial conditions under the presence of uncertainties involving process noises and external disturbances. The indirect model-based approach to adaptation is promoted here as a more suitable strategy for the fast changes that occurs in chaotic systems. In other words, the usual direct adaptive strategies may be too slow to respond to the inherently fast changing dynamics of chaotic systems. Using Lyapunov analysis, the sliding mode approach illustrates the asymptotic convergence of synchronization error to zero as well as good robustness against external disturbances. The interval type-2 structure aims to remedy the undesirable chattering phenomenon that is common in most conventional sliding mode control applications. It also provides a more effective equivalent model in the indirect approach, which leads to improved handling of the chaotic variations and uncertainties. Two numerical pairs of chaotic systems, i.e. the Lorenz and Chen’s systems and the Rössler system and modified Chua’s circuit are considered. In particular, in comparison with its type-1 fuzzy counterpart, the control effort is reduced by an average of 26.25% and 17.4% for the synchronization of the two corresponding systems, respectively. Furthermore, the integral of squared error is also improved by an average of 27.2% and 25.33%. This is while convergence time is reduced to less than 0.5 s and 1.5 s.     

分数阶异结构超混沌系统完全同步与反相同步控制

以分数阶Lü混沌系统和分数阶Chen超混沌系统为例,研究了维数不同、分数阶次不相等的异结构的混沌系统和超混沌系统的完全同步和反相同步.首先,基于分数阶系统稳定性理论和非线性动力学理论,构造出相应的非线性控制器,实现了两个维数不同,分数阶次不相等异结构混沌系统与超混沌系统的完全同步和反相同步;其次,基于分数阶稳定性理论,对上述两类同步给出了严格的数学证明;最后,借助于预估-校正算法,利用数值模拟验证了所提方法的有效性.