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首页> 外文期刊>IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics >Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays
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Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays

机译:具有分布时滞的离散复杂网络的同步和状态估计

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摘要

In this paper, a synchronization problem is investigated for an array of coupled complex discrete-time networks with the simultaneous presence of both the discrete and distributed time delays. The complex networks addressed which include neural and social networks as special cases are quite general. Rather than the commonly used Lipschitz-type function, a more general sector-like nonlinear function is employed to describe the nonlinearities existing in the network. The distributed infinite time delays in the discrete-time domain are first defined. By utilizing a novel Lyapunov-Krasovskii functional and the Kronecker product, it is shown that the addressed discrete-time complex network with distributed delays is synchronized if certain linear matrix inequalities (LMIs) are feasible. The state estimation problem is then studied for the same complex network, where the purpose is to design a state estimator to estimate the network states through available output measurements such that, for all admissible discrete and distributed delays, the dynamics of the estimation error is guaranteed to be globally asymptotically stable. Again, an LMI approach is developed for the state estimation problem. Two simulation examples are provided to show the usefulness of the proposed global synchronization and state estimation conditions. It is worth pointing out that our main results are valid even if the nominal subsystems within the network are unstable.
机译:在本文中,研究了同时存在离散和分布式时延的耦合复杂离散时间网络阵列的同步问题。所解决的复杂网络非常普遍,其中包括神经网络和社交网络。而不是通常使用的Lipschitz型函数,而是采用更通用的扇形非线性函数来描述网络中存在的非线性。首先定义离散时域中的分布式无限时延。通过利用新颖的Lyapunov-Krasovskii泛函和Kronecker产品,证明了在某些线性矩阵不等式(LMI)可行的情况下,具有分布式延迟的定址离散时间复杂网络是同步的。然后针对相同的复杂网络研究状态估计问题,其中目的是设计一个状态估计器,以通过可用的输出测量来估计网络状态,从而对于所有允许的离散和分布式延迟,保证估计误差的动态性在全局上渐近稳定。再次,针对状态估计问题开发了LMI方法。提供了两个仿真示例,以显示所提出的全局同步和状态估计条件的有用性。值得指出的是,即使网络中的标称子系统不稳定,我们的主要结果也是有效的。

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