Wang Zidong, Wang Yao, Liu Yurong
School of Information Science and Technology, Donghua University, Shanghai 200051, China.
IEEE Trans Neural Netw. 2010 Jan;21(1):11-25. doi: 10.1109/TNN.2009.2033599. Epub 2009 Dec 1.
In this paper, the problem of stochastic synchronization analysis is investigated for a new array of coupled discrete-time stochastic complex networks with randomly occurred nonlinearities (RONs) and time delays. The discrete-time complex networks under consideration are subject to: 1) stochastic nonlinearities that occur according to the Bernoulli distributed white noise sequences; 2) stochastic disturbances that enter the coupling term, the delayed coupling term as well as the overall network; and 3) time delays that include both the discrete and distributed ones. Note that the newly introduced RONs and the multiple stochastic disturbances can better reflect the dynamical behaviors of coupled complex networks whose information transmission process is affected by a noisy environment (e.g., internet-based control systems). By constructing a novel Lyapunov-like matrix functional, the idea of delay fractioning is applied to deal with the addressed synchronization analysis problem. By employing a combination of the linear matrix inequality (LMI) techniques, the free-weighting matrix method and stochastic analysis theories, several delay-dependent sufficient conditions are obtained which ensure the asymptotic synchronization in the mean square sense for the discrete-time stochastic complex networks with time delays. The criteria derived are characterized in terms of LMIs whose solution can be solved by utilizing the standard numerical software. A simulation example is presented to show the effectiveness and applicability of the proposed results.
本文研究了一类新的具有随机出现的非线性(RONs)和时滞的耦合离散时间随机复杂网络阵列的随机同步分析问题。所考虑的离散时间复杂网络具有以下特点:1)根据伯努利分布的白噪声序列出现的随机非线性;2)进入耦合项、延迟耦合项以及整个网络的随机干扰;3)包括离散时滞和分布时滞的时滞。需要注意的是,新引入的RONs和多种随机干扰能够更好地反映信息传输过程受噪声环境影响的耦合复杂网络(如基于互联网的控制系统)的动力学行为。通过构造一个新颖的类李雅普诺夫矩阵泛函,应用延迟分解的思想来处理所讨论的同步分析问题。通过结合线性矩阵不等式(LMI)技术、自由加权矩阵方法和随机分析理论,得到了几个依赖于时滞的充分条件,这些条件确保了具有时滞的离散时间随机复杂网络在均方意义下的渐近同步。所推导的准则以LMI的形式给出,其解可以通过使用标准数值软件来求解。给出了一个仿真例子以说明所提结果的有效性和适用性。