网络化马尔可夫跳变系统的鲁棒无源控制

针对基于网络的凸多面体不确定离散时间马尔可夫跳变系统,研究其鲁棒无源控制问题.在网络诱导时滞是时变且有界的情况下,基于李雅普诺夫稳定性理论,通过构造参数依赖的随机李雅普诺夫泛函和运用广义系统变换,提出了不依赖模态的无源控制器存在的时滞依赖充分条件.所设计的鲁棒无源控制器保证了相应的闭环系统是鲁棒随机稳定且具有指定耗散率.将鲁棒无源控制器设计问题转化为一组线性矩阵不等式的可解性问题.仿真算例证明了本文方法的有效性.     

一类非线性时滞网络控制系统的无源性分析

本文研究了一类非线性时滞网络控制系统的无源性问题.利用Lyapunov稳定性理论,结合线性矩阵不等式(LMI)技术,通过构造Lyapunov-Krasovskii泛函,在考虑两种不同时滞的情况下,获得了系统满足无源性的充分条件,最后通过仿真算例验证了结论的正确性和方法的有效性.     

Markov切换时滞基因调控网络的均方同步和随机无源同步北大核心CSCD

基因调控网络(GRNs)及其动力学模型的研究在后基因组时代是一个重要的研究领域.定性分析基因调控网络及其动力学对系统地认识生物体具有重要意义.该文提出了一类具有时变时滞和Markov切换的随机基因调控网络模型,研究了其均方同步和随机无源同步问题.通过设计合适的Lyapunov-Krasovskii泛函(LKF),并利用Lyapunov稳定性理论、线性矩阵不等式方法和随机分析技巧,得到了均方同步和随机无源同步的充分条件.此外,通过与其他文献进行比较,显示了该文结果的理论价值.数值模拟验证了所得充分条件的有效性.     

基于事件驱动的拉格朗日无源系统的输出同步控制

针对由多个拉格朗日无源系统构成的分布式网络系统,利用基于事件驱动的方法来解决系统的输出同步问题.在拓扑网络为强连通图的条件下,推导出无通信时延的拉格朗日无源系统网络输出同步的事件触发条件.进一步,利用网络设置框架,对于有通信时延的拉格朗日无源系统网络推导出输出同步的事件触发条件.文章将事件驱动控制方法应用到分布式无源网络系统的协同控制中,并将系统的拓扑网络由平衡图延伸到了更为一般的强连通图.     

中药配伍系统(CSCMM)的复杂网络拓扑特征分析

通过构建表征中药配伍系统(CSCMM)的复杂网络,按照整体论、系统论的思想,运用复杂网络的理论,借助整体网络的分析技术和计算机可视化算法对CSCMM进行了宏观综合的量化分析和定性阐释,揭示了该网络诸如小世界和近似无标度等网络拓扑特征,以期为中药配伍的现代化研究提供一定的思考和启发.     

具有不确定性的分数阶时滞复值神经网络无源性

该文研究了一类具有不确定性和时滞的分数阶复值神经网络无源性问题,未将复值神经网络模型拆分成两个实值系统,而是将复值系统当成一个整体直接进行处理.通过构造恰当的Lyapunov函数,并利用矩阵不等式技巧,建立了网络无源性的线性矩阵不等式判据.给出的数值例子和仿真验证了获得结论的可行性和有效性.     

M-G-P型复杂网络中有寿命节点的进入退出机制

现实中复杂网络结构复杂,形式多样,处在高度动态变化的过程.为了更好地理解真实网络的演化,基于复杂网络的特性进行分析,建立了Poissotn连续时间增长节点具有寿命的M-G-P型复杂网络模型,模型中包括:新节点加入、节点老化和老节点退出等,基于齐次马尔可夫链对模型的度分布进行计算,得出M-G-P型网络的度分布符合幂律分布,模型和BA模型一样能产生指数γ=3的无标度网络,验证了导致无标度网络度分布特征起关键性作用的是链接的偏好特性.     

层间单向耦合星形圆环状网络的同步能力

多层复杂网络同步是网络科学研究的一个前沿方向,目前对多层复杂网络同步性的研究大多集中在无向多层复杂网络上,而更加贴近于实际的多层有向网络研究很少.首先根据主稳定方程(MSF)严格计算出M层层间单向耦合星形圆环状网络的超拉普拉斯矩阵的特征值谱,并得到反映M层层间单向耦合星形圆环状网络同步能力的重要指标,其次讨论了M层层间单向耦合星形圆环状网络在同步域为有界和无界的两种情况下同步能力与层数、节点数、层间耦合强度和层内耦合强度及中心节点耦合强度之间的关系.最后通过数值模拟给出了层间单向耦合星形圆环状网络同步能力的仿真图像,验证了理论结果的有效性.     

复杂网络统计力学的差分方程方法

通过分析几种估计增长网络度分布方法的缺点,提出估计度分布的差分方程方法,不仅避免了复杂网络分析中将离散问题连续化带来的逻辑矛盾,也避免了网络稳态度分布存在性的假设.利用这个方法给出Poisson增长择优连接网络的度分布公式,借助Poisson过程理论和Gamma 分布的性质严格证明Poisson增长择优连接网络是无标度网络.     

探讨几类具有完全图核心的网络模型的优美性

在复杂网络研究中,人们需要建立网络模型,无标度图就是这样的一种网络模型.我们发现具有完全图核心的网络模型可以演变成无标度图.具有完全图核心的几种网络模型的优美性得到研究.     

Robust passivity analysis of fuzzy Cohen-Grossberg BAM neural networks with time-varying delays

This paper is concerned with the problem of passivity analysis for a class of Cohen-Grossberg fuzzy bidirectional associative memory (BAM) neural networks with time varying delay. By employing the delay fractioning technique and linear matrix inequality optimization approach, delay dependent passivity criteria are established that guarantees the passivity of fuzzy Cohen-Grossberg BAM neural networks with uncertainties. The passivity condition is expressed in terms of LMIs, which can be easily solved by various convex optimization algorithms. Finally, a numerical example is given to illustrate the effectiveness of the proposed result.     

Passivity analysis for neural networks of neutral type with Markovian jumping parameters and time delay in the leakage term

In this paper, the problem of passivity analysis is investigated for neutral type neural networks with Markovian jumping parameters and time delay in the leakage term. The delay is assumed to be time-varying and belong 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, new delay-dependent passivity conditions are derived in terms of linear matrix inequalities (LMIs). Moreover, it is well known that the passivity behavior of neural networks is very sensitive to the time delay in the leakage term. Finally, three numerical examples are given to show the effectiveness and less conservatism of the proposed method.     

New robust passivity criteria for stochastic fuzzy BAM neural networks with time-varying delays

In this paper, we consider the problem of passivity analysis issue for a class of stochastic fuzzy BAM neural networks with time varying delays. By employing the idea of delay-fractioning technique and Lyapunov stability theory, a new set of sufficient conditions are derived in terms of linear matrix inequalities for obtaining the passivity condition of the considered neural network model. First, we derive the passivity condition for stochastic fuzzy BAM neural networks with time varying delays and then the result is extended to the case with uncertainties. Two numerical examples are given to illustrate the effectiveness and conservatism of the obtained results.     

Passivity analysis of neural networks with Markovian jumping parameters and interval time-varying delays

In this paper, the problem of passivity analysis is investigated for neural networks with Markovian jumping parameters, interval time-varying delays and norm bounded parameter uncertainties. The delay-dependent passivity conditions are derived for two types of interval time-varying delay in terms of linear matrix inequalities (LMIs). Finally, three numerical examples are given to show the effectiveness of the proposed conditions.     

New delay‐dependent global robust passivity analysis for stochastic neural networks with Markovian jumping parameters and interval time‐varying delays

The problem of passivity analysis for stochastic neural networks with Markovian jumping parameters and interval time‐varying delays is investigated in this article. By constructing a novel Lyapunov–Krasovskii functional based on the complete delay‐decomposing idea and using improved free‐weighting matrix method, some improved delay‐dependent passivity criteria are established in terms of linear matrix inequalities. Numerical examples are also given to show the effectiveness of the proposed methods. © 2015 Wiley Periodicals, Inc. Complexity 21: 167–179, 2016     

Global Robust Passivity Analysis for Stochastic Interval Neural Networks with Interval Time-Varying Delays and Markovian Jumping Parameters

In this paper, the problem of passivity analysis is investigated for stochastic interval neural networks with interval time-varying delays and Markovian jumping parameters. By constructing a proper Lyapunov-Krasovskii functional, utilizing the free-weighting matrix method and some stochastic analysis techniques, we deduce new delay-dependent sufficient conditions, that ensure the passivity of the proposed model. These sufficient conditions are computationally efficient and they can be solved numerically by linear matrix inequality (LMI) Toolbox in Matlab. Finally, numerical examples are given to verify the effectiveness and the applicability of the proposed results.     

Passivity analysis of Markovian switching complex dynamic networks with multiple time-varying delays and stochastic perturbations

This paper is concerned with the passivity problem for a class of Markovian switching complex dynamic networks with multiple time-varying delays and stochastic perturbations. Some sufficient conditions are obtained to guarantee that the complex dynamic networks with multiple time-varying delays and stochastic perturbations under Markovian switching are passive in the sense of expectation. The appropriate stochastic Lyapunov–Krasovskii functional was constructed, and stochastic theory, linear matrix inequality technique and properties of Weiner process were employed to achieve the results. Finally, some simulation examples are presented to illustrate the effectiveness of the obtained results.     

New results on exponential passivity of neural networks with time-varying delays

The problem of delay-dependent exponential passivity analysis is investigated for neural networks with time-varying delays. By use of a linear matrix inequality (LMI) approach, a new exponential passivity criterion is proposed via the full use of the information of neuron activation functions and the involved time-varying delays. The obtained results have less conservativeness and less number of decision variables than the existing ones. A numerical example is given to demonstrate the effectiveness and the reduced conservatism of the derived results.     

Passivity-based control for Hopfield neural networks using convex representation

This paper considers the problem of passivity-based controller design for Hopfield neural networks. By making use of a convex representation of nonlinearities, a feedback control scheme based on passivity and Lyapunov theory is presented. A criterion for existence of the controller is given in terms of linear matrix inequality (LMI), which can be easily solved by a convex optimization problem. An example and its numerical simulation are given to show the effectiveness of the proposed method.     

A new augmented Lyapunov-Krasovskii functional approach to exponential passivity for neural networks with time-varying delays

In this paper, the problem of exponential passivity analysis for uncertain neural networks with time-varying delays is considered. By constructing new augmented Lyapunov-Krasovskii’s functionals and some novel analysis techniques, improved delay-dependent criteria for checking the exponential passivity of the neural networks are established. The proposed criteria are represented in terms of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms. A numerical example is included to show the superiority of our results.