IEEE Trans Neural Netw Learn Syst. 2020 Dec;31(12):5483-5496. doi: 10.1109/TNNLS.2020.2968342. Epub 2020 Nov 30.
In the literature, the effects of switching with average dwell time (ADT), Markovian switching, and intermittent coupling on stability and synchronization of dynamic systems have been extensively investigated. However, all of them are considered separately because it seems that the three kinds of switching are different from each other. This article proposes a new concept to unify these switchings and considers global exponential synchronization almost surely (GES a.s.) in an array of neural networks (NNs) with mixed delays (including time-varying delay and unbounded distributed delay), switching topology, and stochastic perturbations. A general switching mechanism with transition probability (TP) and mode-dependent ADT (MDADT) (i.e., TP-based MDADT switching in this article) is introduced. By designing a multiple Lyapunov-Krasovskii functional and developing a set of new analytical techniques, sufficient conditions are obtained to ensure that the coupled NNs with the general switching topology achieve GES a.s., even in the case that there are both synchronizing and nonsynchronizing modes. Our results have removed the restrictive condition that the increment coefficients of the multiple Lyapunov-Krasovskii functional at switching instants are larger than one. As applications, the coupled NNs with Markovian switching topology and intermittent coupling are employed. Numerical examples are provided to demonstrate the effectiveness and the merits of the theoretical analysis.
在文献中,平均驻留时间(ADT)切换、马尔可夫切换和间歇耦合对动态系统稳定性和同步的影响已经得到了广泛的研究。然而,它们都是分别考虑的,因为这三种切换似乎彼此不同。本文提出了一个新的概念来统一这些切换,并考虑了具有混合时滞(包括时变时滞和无界分布时滞)、切换拓扑和随机扰动的神经网络(NNs)阵列中的全局指数同步几乎必然(GES a.s.)。引入了一种具有转移概率(TP)和模式相关平均驻留时间(MDADT)的通用切换机制(即本文中的基于 TP 的 MDADT 切换)。通过设计一个多李雅普诺夫-克拉索夫斯基函数并开发一组新的分析技术,获得了确保具有一般切换拓扑的耦合 NNs 实现 GES a.s.的充分条件,即使在存在同步和非同步模式的情况下也是如此。我们的结果消除了多李雅普诺夫-克拉索夫斯基函数在切换瞬间的增量系数大于一的限制条件。作为应用,采用了具有马尔可夫切换拓扑和间歇耦合的耦合 NNs。提供了数值示例来验证理论分析的有效性和优点。