具有时滞的二阶Hopfield神经网络的稳定性分析

对具有时滞的二阶Hopfield型神经网络平衡点的全局渐近稳定性问题进行了研究。在不要求连接权矩阵的对称性和输入输出函数的可微性与单调性的情况下 ,通过构造适当的Lyapunov泛函得到了网络平衡点的存在性和全局渐近稳定性的若干充分条件 ,这些条件可用于设计全局渐近稳定的二阶人工神经网络。     

Global Asymptotic Synchronization of a Class of BAM Neural Networks with Time Delays via Integrating Inequality Techniques

In this paper, the authors are concerned with global asymptotic synchronization for a class of BAM neural networks with time delays. Instead of using Lyapunov functional method, LMI method and matrix measure method which are recently widely applied to investigating global exponential/asymptotic synchronization for neural networks, two novel sufficient conditions on global asymptotic synchronization of above BAM neural networks are established by using a kind of new study method of global synchronization: Integrating inequality techniques. The method and results extend the study of global synchronization of neural networks.     

General Decay Synchronization for Recurrent Neural Networks with Mixed Time Delays

This paper studies the general decay synchronization(GDS) of a class of recurrent neural networks(RNNs) with general activation functions and mixed time delays. By constructing suitable Lyapunov-Krasovskii functionals and employing useful inequality techniques, some sufficient conditions on the GDS of considered RNNs are established via a type of nonlinear control. In addition, one example with numerical simulations is presented to illustrate the obtained theoretical results.     

离散和分布时变时滞混沌神经网络广义投影同步

研究一类离散和分布时变时滞的混沌神经网络的广义投影同步问题.利用非线性观测器方法实现同步十分简单,且利用极点配置技术,可通过调整特征值来调节同步速率的快慢.与一般混沌神经网络模型相比,带有离散和分布时变时滞的混沌神经网络模型更为一般,同时反同步和完全同步是广义投影同步的特例.最后,提出基于广义投影同步的离散和分布时变时滞混沌神经网络的保密通信方案,给出两个数值仿真例子验证结果的有效性.     

变时滞广义神经网络的指数稳定性

讨论具有可变时滞的Cohen Grossberg广义神经网络平衡态的稳定性问题。以推广的Halanay微分不等式为工具,通过构造合适的Lyapunov函数,结合Dini导数和不等式技巧,对具有可变时滞(适合于任何时滞有界情况)的广义神经网络平衡态的指数稳定性进行分析。在适当的假设条件下,得到了系统渐近指数稳定的几个充分性判据,与此同时也给出了相应的Lyapunov指数。     

一类混合时滞系统的混沌同步与应用

针对一类混合时滞系统的同步性问题,提出了这类系统混沌同步的一般化结果.利用Lyapunov函数,结合不等式技巧及驱动-响应方法,通过一个控制律来实现两个可辨识混合时滞系统的状态同步,并获得了实用的构造性代数条件,使得混沌同步的研究结果一般化,推广了已有结果.讨论了所获得的结果在生物学及神经网络中的应用,得到相应的同步控制充分性条件,进一步扩展了已有结果.     

Finite-Time Synchronization for a Class of Dynamical Complex Networks with Nonidentical Nodes and Uncertain Disturbance

This paper investigates the finite-time synchronization for a class of linearly coupled dynamical complex networks with both nonidentical nodes and uncertain disturbance. A set of controllers are designed such that the considered system can be finite-timely synchronized onto the target node.Based on the stability of the error equation, the Lyapunov function method and the linear matrix inequality technique, several sufficient conditions are derived to ensure the finite-time synchronization,and applied to the case of identical nodes and the one without uncertain disturbance. Also the adaptive finite-time synchronization is discussed. A numerical example is given to show the effectiveness of the main results obtained.     

Almost Automorphic Solutions for Quaternion-Valued Hopfield Neural Networks with Mixed Time-Varying Delays and Leakage Delays

This paper considers a class of quaternion-valued Hopfield neural networks with mixed time-varying delays and leakage delays. By utilizing the exponential dichotomy of linear differential equations, Banach's fixed point theorem and differential inequality techniques, the authors obtain some sufficient conditions to ensure the existence and global exponential stability of almost automorphic solutions for this class of quaternion-valued neural networks. The results are completely new. Finally,the authors give an example to illustrate the feasibility of the results.     

Exponentially adaptive synchronization of an uncertain delayed dynamical network

Over the past decades, complex networks have been prosperous greatly in various fields of sciences and engineering. Much attention has been given to investigate the synchronization of complex networks in recent years. However, few work has done for the networks with uncertain parameters and unknown topology. In this paper, to further reveal the dynamical mechanism in complex networks with time delays, an uncertain general complex dynamical network with delayed nodes is studied. By constructing a drive network and a suitable slave network, several novel criteria for the networks consisting of the identical nodes and different nodes have been obtained based on the adaptive feedback method. Particularly, the hypotheses and the proposed adaptive laws for network synchronization are simple and can be readily applied in practical applications. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed synchronization criteria.     

一类延时细胞神经网络的指数周期性与稳定性

细胞神经网络动态行为的研究是细胞神经网络应用的理论基础。对一类具分布延时细胞神经网络,研究了其全局指数周期性与稳定性。在输出函数满足全局Lipschitz连续的条件下,通过构造合适的Lyapunov泛函,给出了延时细胞神经网络全局指数周期性与稳定性的容易验证的充分条件。给出了算例及其仿真结果来验证所得结论,并说明所得结论与文献[16]的结论是相互独立的。     

Existence and Global Exponential Stability of Pseudo Almost Periodic Solutions of a General Delayed BAM Neural Networks

This paper studies a class of general BAM neural networks with multiple delays. Employing the exponential dichotomy theory and fixed point method, together with constructing suitable Lyapunov functionals, easily verifiable delay-independent criteria are established to ensure the existence and global exponential stability of pseudo almost periodic solutions, which not only generalize but also complement some existing ones. These theoretical results are also supported with numerical simulations.     

Pinning Synchronization of Discrete-Time Complex Networks with Different Time-Varying Delays

This paper investigates pinning synchronization of discrete-time complex networks with different time-varying delays. An important lemma is presented and proved, then detailed analysis is given to yield some synchronization criteria for this kind of networks. The results provide an effective way to synchronize discrete-time complex networks by reducing control cost. Furthermore, these theoretical results are illustrated by a complex network via two kinds of pinning schemes. Numerical simulations verify the feasibility of the proposed methods.     

具变时延细胞神经网络模型的全局指数稳定性

通过构造新的Lyapunov泛函．巧妙引入可调实参数，并结合Hardy不等式以及推广的Halanay时延微分不等式，讨论了一类具有可变时延细胞神经网（DCNNs）的全局指数稳定性问题，所得结果改进、推广了文[7]、文[8]中相应的结论，并可应用于以前所不能处理的若干情形。特别地，还通过实例说明了相应准则的应用，扩大了神经网络设计的范围，这在理论上和应用中都有着重要意义。     

Passivity analysis for uncertain stochastic neural networks with discrete interval and distributed time-varying delays

The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying delays. The parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belongs to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique, new delay dependent passivity conditions are derived in terms of linear matrix inequalities (LMIs). Finally, numerical examples are given to show the less conservatism of the proposed conditions.     

New Results on Interval General Cohen-Grossberg BAM Neural Networks

This paper is concerned with an interval general Cohen-Grossberg bidirectional associative memory neural networks with mixed delays. Under proper conditions, the authors studied the existence,the uniqueness and the global exponential stability of almost automorphic solutions for the suggested system. The proposed method was mainly based on the exponential dichotomy of linear differential equation, the Banach's fixed point principle and the differential inequality techniques. The authors illustrate with an example to demonstrate the effectiveness of the proposed findings.     

On the exponential stability of neural networks systems with time-varying delay

In this paper, exponential stability of Hopfield-type neural networks with time-varying delays are analyzed. By using the Lyapunov functional method, sufficient conditions are obtained for general exponential stabilities. At the same time, the output functions do not satisfy the Lipschitz conditions and do not require them to be differential or strictly monotonously increasing. Moreover, all results are established without assuming any symmetry of the connection matrix.A numeric example is pressented to show the effective of these criteria.     

A Secure Clock Synchronization Scheme for Wireless Sensor Networks Against Malicious Attacks

Journal of Systems Science and Complexity - This paper studies the secure and accurate clock synchronization problem for sensor networks with time-varying delays and malicious attacks. A novel...     

具有瞬时耦合及通讯时滞的调和振子同步动力学

针对具有局部瞬时耦合及通讯延时的调和振子动力网络系统,给出了一种分布式同步算法。分别在无向固定拓扑和切换拓扑两种情形下给出了同步态的解析表达式,以及耦合系统的解析解,并给出了一些一般而简单的同步化准则。结果表明网络化的调和振子在比较弱的瞬时网络连通条件下,可以达到同步状态。数值模拟表明所给理论结果的正确性和有效性。     

Finite-time tracking control for motor servo systems with unknown dead-zones

A finite-time tracking control scheme is proposed in this paper based on the terminal slid- ing mode principle for motor servo systems with unknown nonlinear dead-zone inputs. By using the differential mean value theorem, the dead-zone is represented as a time-varying system and thus the inverse compensation approach is avoided. Then, an indirect terminal sliding mode control （ITSMC） is developed to guarantee the finite-time convergence of the tracking error and to overcome the singu- larity problem in the traditional terminal sliding mode control. In the proposed controller design, the unknown nonlinearity of the system is approximated by a simple sigmoid neural network, and the ap- proximation error is diminished by employing a robust term. Comparative experiments on a turntable servo system are conducted to show the superior performance of the proposed method.     

基于时变延迟混沌神经网络的H∞同步保密通信

针对一类含保密信息的时变延迟混沌神经网络,提出了同结构H_∞同步控制方案, 在实现同步后能有效恢复出隐藏的多路明文信号。利用线性矩阵不等式方法为同结构时变延迟混沌神经网络设计了H_∞同步控制器,在此基础上应用Lyapunov方法分析了同步误差的收敛性。当同步误差收敛时,可根据被动系统状态恢复出所传输的隐藏信号。仿真结果表明,该控制方案可以实现变时延混沌神经网络同步,并能恢复出多路明文信号。