Guo Yuru, Wang Zidong, Li Jun-Yi, Xu Yong
IEEE Trans Neural Netw Learn Syst. 2025 Jun;36(6):10450-10463. doi: 10.1109/TNNLS.2024.3448376.
In this article, we consider the impulsive estimation problem for a specific category of discrete-time complex networks (CNs) characterized by Markovian switching topologies. The measurement outputs of the underlying CNs, transmitted to the observer over wireless networks, are subject to bit rate constraints. To effectively reduce the estimation error and enhance estimation performance, a mode-dependent impulsive observer is proposed that employs the impulse mechanism. The application of stochastic analysis techniques leads to the derivation of a sufficient condition for ensuring the mean-square boundedness of the estimation error dynamics. The upper bound of the error is then analyzed by iteratively exploring the Lyapunov relation at both impulsive and non-impulsive instants. Moreover, an optimization algorithm is presented for handling the bit rate allocation, which is coupled with the design of desired observer gains using the linear matrix inequality (LMI) approach. Within this theoretical framework, the relationship between the mean-square estimation performance and the bit rate allocation protocol is further elucidated. Finally, a simulation example is provided to demonstrate the validity and effectiveness of the proposed impulsive estimation approach.
在本文中，我们考虑一类具有马尔可夫切换拓扑结构的离散时间复杂网络（CNs）的脉冲估计问题。底层复杂网络的测量输出通过无线网络传输给观测器，这些输出受到比特率约束。为了有效降低估计误差并提高估计性能，提出了一种采用脉冲机制的依赖模式的脉冲观测器。随机分析技术的应用导致推导出一个确保估计误差动态均方有界的充分条件。然后通过在脉冲和非脉冲时刻迭代探索李雅普诺夫关系来分析误差的上界。此外，提出了一种用于处理比特率分配的优化算法，该算法使用线性矩阵不等式（LMI）方法与期望观测器增益的设计相结合。在这个理论框架内，进一步阐明了均方估计性能与比特率分配协议之间的关系。最后，提供了一个仿真示例来证明所提出的脉冲估计方法的有效性和实用性。
