Liu Fen, Meng Wei, Yao Deyin
IEEE Trans Neural Netw Learn Syst. 2023 Nov;34(11):8250-8261. doi: 10.1109/TNNLS.2022.3148194. Epub 2023 Oct 27.
The bounded antisynchronization (AS) problem of multiple discrete-time neural networks (NNs) based on the fuzzy model is studied, in consideration of the differences in quantity and communication among different NN groups, the variabilities of dynamics, and communication topological affected by environments. To reduce the energy consumption of communication, a cluster pinning communication mechanism is proposed, and an impulsive observer is designed to estimate the state of target NN. Then, a multilevel hybrid controller based on the impulsive observer is built including the AS controller and the bounded synchronization (BS) controller. Sufficient conditions for bounded AS are obtained by analyzing the stability of the BS augmented error (BSAE) and the AS augmented error (ASAE) based on the fuzzy-based Lyapunov functional (FBLF). Finally, a numerical example and an application example are given to verify the validity of the obtained results.
研究了基于模糊模型的多个离散时间神经网络（NNs）的有界反同步（AS）问题，考虑到不同神经网络组之间数量和通信的差异、动力学的变化以及受环境影响的通信拓扑结构。为了降低通信能耗，提出了一种簇钉扎通信机制，并设计了一个脉冲观测器来估计目标神经网络的状态。然后，基于脉冲观测器构建了一个多级混合控制器，包括AS控制器和有界同步（BS）控制器。基于模糊李雅普诺夫泛函（FBLF），通过分析BS增广误差（BSAE）和AS增广误差（ASAE）的稳定性，得到了有界AS的充分条件。最后，给出了一个数值例子和一个应用例子来验证所得结果的有效性。
