Zhong Xinyu, Gao Yanbo
IEEE Trans Neural Netw Learn Syst. 2021 Nov;32(11):4916-4930. doi: 10.1109/TNNLS.2020.3026163. Epub 2021 Oct 27.
This article addresses the quantized sampled-data (QSD) synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays, in which the sampled-data control and state quantization effect have been considered. By utilizing a proper variable substitution to transform the original system into a first-order differential system, choosing a new Lyapunov-Krasovskii functional (LKF) containing both the continuous terms and the discontinuous terms, and applying Jensen inequality and an improved reciprocally convex inequality to estimate the derivative of the LKF, the sufficient conditions for QSD synchronization for INNs are newly obtained in terms of linear matrix inequalities (LMIs), and the desired QSD controllers are designed by solving a set of LMIs. Finally, three numerical examples are provided to validate the effectiveness and benefit of the proposed results.
本文研究了具有异质时变延迟的惯性神经网络（INN）的量化采样数据（QSD）同步问题，其中考虑了采样数据控制和状态量化效应。通过利用适当的变量代换将原系统转化为一阶微分系统，选择一个同时包含连续项和不连续项的新的Lyapunov-Krasovskii泛函（LKF），并应用Jensen不等式和一个改进的互反凸不等式来估计LKF的导数，以线性矩阵不等式（LMI）的形式新获得了INN的QSD同步的充分条件，并通过求解一组LMI设计了所需的QSD控制器。最后，给出了三个数值例子来验证所提结果的有效性和优越性。
